GB2587769A

GB2587769A - Method and system for updating auto-setting of cameras

Info

Publication number: GB2587769A
Application number: GB1801110.6A
Authority: GB
Inventors: Citerin Johann; Kergourlay Gérald; Tannhauser Falk; Visa Pierre
Original assignee: Canon Inc
Current assignee: Canon Inc
Priority date: 2018-01-23
Filing date: 2018-01-23
Publication date: 2021-04-14
Anticipated expiration: 2038-01-23
Also published as: GB201801110D0; GB2587769B

Abstract

A method for setting the parameters of a camera comprises the steps of: Obtaining a first set of image characteristic values of images captured at a first lighting condition, the image characteristics being dependent on the camera parameters; determining at least one second set of image characteristic values by adapting values of the first set of image characteristic values from images captured at a second lighting condition; selecting parameter values for the camera, based on image characteristic values determined as a function of at least the second set of image characteristics; modifying settings of the camera as a function of the selected parameter values; comparing the first and the second lighting condition to determine whether the lighting conditions have significantly changed thereby requiring the use of new parameter settings; obtaining a third set of image characteristic values of images captured at a third lighting condition, wherein the steps of determining, selecting and modifying are performed again using the third set of image characteristic values instead of the first set when it is determined during the comparison step that the lighting conditions have significantly changed.

Description

METHOD AND SYSTEM FOR UPDATING AUTO-SETTING OF CAMERAS

FIELD OF THE INVENTION

The present invention relates to the technical field of camera setting and to a method and a system for updating auto-setting of cameras, for example updating auto-setting of cameras within video surveillance systems.

BACKGROUND OF THE INVENTION

Video surveillance is currently a fast-growing market tending to become increasingly widespread for ubiquitous applications. It can be used today in numerous areas such as crime prevention, private and public areas for security purposes, abnormal event detection, traffic monitoring, customer behaviour, or general data gathering.

The ever-increasing use of network cameras for such purposes has led in particular to increasing image quality, especially to improving image resolution, contrast, and colour.

However, it has been observed that image quality improvement is slowing recently.

Indeed, while the camera sensors embedded in recent cameras may provide high quality outputs, image quality highly depends on camera settings that are often not optimal. Motion blur, bad exposure, and a wrong choice of network settings lead very often to poor images. Moreover, it is noted that environmental conditions may change significantly over a few hours. For example, day versus night, rain versus sun, and light intensity changes are typical environmental changes that have a huge impact on image quality and resource consumption. Therefore, using only one fixed camera setting leads to very poor image quality on average.

To address such changes of environmental conditions, there exist in-camera auto-setting methods such as auto-focus and auto-exposure for adapting camera settings dynamically. Such an auto-setting capability may be further improved thanks to additional manual settings and profiles, making it possible to adapt the auto-setting to the particular camera environment and to choose a suitable trade-off, e.g. a suitable trade-off between image quality and network consumption.

Below, the in-camera embedded auto-setting is referred to as the "camera auto-mode" or the "auto-mode".

Although the camera auto-mode makes it possible to improve image quality by adapting camera settings dynamically, the settings may still be improved. In particular, the camera auto-mode is not so reliable for the following reasons: - fine-tuning camera settings to improve the quality of the auto-mode is time-consuming and requires particular skills and a good knowledge of the camera's capabilities and settings interface; - most camera installers do not modify the settings and keep with the default factory auto-mode; - some issues such as motion blur are not solvable through auto-setting; very few Of any) camera auto-modes are dedicated to optimizing the image in a region of interest (ROI), which leads to bad exposure issues and suboptimal quality; and the camera auto-mode is not adapted to specific tasks or missions, which do not necessarily have the same constraints as the mainstream usage that the camera auto-mode is suited for.

Moreover, it is noted that the quality of images obtained from network cameras as well as deployment ease and cost of the latter would benefit from a more effective auto-setting.

This would make it possible for non-specialists, e.g. by the customer's staff itself, to install cameras and this should be efficient in any situation.

It is to be recalled that the three main physical settings that are used to control the quality of images obtained from a camera, in terms of contrast, brightness, sharpness (or blur), and noise level are the aperture, the gain, and the shutter speed (corresponding to the exposure time, generally expressed in seconds).

Generally, the camera auto-mode determines values for the aperture, the gain, and the shutter speed as a function of contrast and global exposure analysis criteria. Many combinations of aperture, gain, and shutter speed values lead to the same contrasts. Indeed, increasing the aperture value, the gain value, and/or increasing the shutter speed value (i.e. increasing exposure time) results in a brighter image. However, increasing the aperture value, the gain value, and/or the shutter speed value does not result only in a brighter image but also affects depth-of-field, noise, and motion blur: increasing the aperture value means increasing the amount of light that reaches the sensor, which results in a brighter image but also in an image having a smaller depth-of-field (which increases the defocus blur); increasing the gain value means increasing the dynamic of the image, which results in a brighter image but also in an image having more noise; and increasing the shutter speed value (i.e. increasing the exposure time) means increasing the amount of light that reaches the sensor, which results in a brighter image, but also increasing the motion blur.

Accordingly, a trade-off should be reached between the aperture, gain, and shutter speed values so as to maximize the contrast while minimizing noise and blur (defocus blur and motion blur).

However, since most network cameras monitor distant targets, the aperture value is generally set so that focus is achieved for any objects positioned more than about one meter from the cameras. As a result, the trade-off to be attained is mainly directed to gain and shutter speed that is to say to noise and motion blur. It is made on assumptions and arbitrary choices which are deemed to meet the environmental conditions of the real scene associated with the field of view of the corresponding camera, but which actually do not.

Consequently, there is a need to improve auto-setting of cameras, in particular for dynamically configuring cameras of video-surveillance systems, taking into account changes of environmental conditions, while disrupting the system as little as possible when it is running.

SUMMARY OF THE INVENTION

The present invention has been devised to address one or more of the foregoing concerns.

In this context, there is provided a solution for improving auto-setting of cameras, for example for improving auto-setting of cameras in video surveillance systems.

According to a first object of the invention, there is provided a method for improving settings of a camera, the method comprising: obtaining a first set of image characteristic values (/,,,f) of images captured by the camera at a first lighting condition, the image characteristics being dependent on the camera parameters (G, S), at least two image characteristic values of the first set respectively corresponding to at least two different values of a same camera parameter; determining at least one second set of image characteristic values (/ cur(ent) by adapting values of the obtained first set of image characteristic values from images captured by the camera at at least one second lighting condition, at least two image characteristic values of the second set corresponding respectively to at least two different values of a same camera parameter; selecting camera parameter values (G, S) for the camera, based on image.curren., characteristic values (contrast A determined as a function of at least the second set of image characteristics(7 and vcurrent); modifying settings of the camera as a function of the selected camera parameter values, the method further comprising a step of comparing the first and the second lighting condition and a step of obtaining a third set of image characteristic values (foal) of images captured by the camera at a third lighting condition, at least two image characteristic values of the third set respectively corresponding to the said same camera parameter, the third set of image characteristic values being used in lieu of the first set as a function of the comparison step. According to the method of the invention, selecting camera parameter values of a camera is rapid, efficient and minimally-invasive for the camera (i.e. the camera does not freeze during the auto-setting and remains operational or freezes for a very short time, preferably after an authorization from a user).

Optional features of the invention are further defined in the dependent appended claims.

According to a second object of the invention, there is provided a device for improving settings of a camera, the device comprising a microprocessor configured for carrying out the steps of: obtaining a first set of image characteristic values (Ica!) of images captured by the camera at a first lighting condition, the image characteristics being dependent on the camera parameters (G, S), at least two image characteristic values of the first set respectively corresponding to at least two different values of a same camera parameter; determining at least one second set of image characteristic values (1 cur(ent) by adapting values of the obtained first set of image characteristic values from images captured by the camera at at least one second lighting condition, at least two image characteristic values of the second set corresponding respectively to at least two different values of a same camera parameter; selecting camera parameter values (G, S) for the camera, based on image characteristic values (contra st,,,-""t) determined as a function of at least the second set of image characteristics (/current); and modifying settings of the camera as a function of the selected camera parameter values, the microprocessor being further configured for carrying out a step of comparing the first and the second lighting condition and a step of obtaining a third set of image characteristic values (68,) of images captured by the camera at a third lighting condition, at least two image characteristic values of the third set respectively corresponding to the said same camera parameter, the third set of image characteristic values being used in lieu of the first set as a function of the comparison step.

The advantages provided by this second object are similar to those described in relation with the first object. Optional features of this second object are further defined in the dependent appended claims.

At least parts of the methods according to the invention may be computer implemented. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit", "module" or "system". Furthermore, the present invention may take the form of a computer program product embodied in any tangible medium of expression having computer usable program code embodied in the medium.

Since the present invention can be implemented in software, the present invention can be embodied as computer readable code for provision to a programmable apparatus on any suitable carrier medium. A tangible carrier medium may comprise a storage medium such as a floppy disk, a CD-ROM, a hard disk drive, a magnetic tape device or a solid state memory device and the like. A transient carrier medium may include a signal such as an electrical signal, an electronic signal, an optical signal, an acoustic signal, a magnetic signal or an electromagnetic signal, e.g. a microwave or RF signal.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features and advantages of the invention will become apparent from the following description of non-limiting exemplary embodiments, with reference to the appended drawings, in which: Figure 1 schematically illustrates an example of a video surveillance system wherein embodiments of the invention may be implemented; Figure 2 is a schematic block diagram of a computing device for implementing embodiments of the invention; Figure 3 is a block diagram illustrating an example of an auto-setting method making it possible to set automatically parameters of a source device according to embodiments of the invention; Figure 4 is a block diagram illustrating a first example of steps carried out during a calibration phase of an auto-setting method as illustrated in Figure 3; Figure 5 illustrates an example of the distribution of the target velocity; Figures 6a, 6b, and 6c illustrate examples of steps for determining new camera settings during the operational use of a camera, without perturbing the use of the camera; Figure 7 is a block diagram illustrating a second example of steps carried out during a calibration phase of an auto-setting method as illustrated in Figure 3; Figures 8 and 9 are sequence diagrams illustrating an example of steps carried out during a calibration phase of an auto-setting method as illustrated in Figure 3; and Figure 10 is a sequence diagram illustrating an example of steps carried out during an operation phase of an auto-setting method as illustrated in Figure 3.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

According to embodiments, a new auto-setting method is provided. It comprises several phases among which a learning phase and a calibration phase for obtaining information and an operation phase for dynamically auto-setting a camera in any situation, when environmental conditions change. A new calibration phase may be triggered when environmental conditions change significantly and when items of information obtained during previous calibration phases are no longer efficient.

Figure 1 schematically illustrates an example of a video surveillance system wherein embodiments of the invention may be implemented.

Video surveillance system 100 includes a plurality of network cameras denoted 110a, 110b, and 110c, for example network cameras of the Internet Protocol (IP) type, generically referred to as IP cameras 110.

Network cameras 110, also referred to as source devices, are connected to a central site 140 via a backbone network 130. In a large video surveillance system, backbone network 130 is typically a wide area network (WAN) such as the Internet.

According to the illustrated example, central site 140 comprises a video manager system (VMS) 150 used to manage the video surveillance system, an auto-setting server 160 used to perform an automatic setting of cameras 110, and a set of recording servers 170 configured to store the received video streams, a set of video content analytics (VCA) sewers configured to analyse the received video streams, and a set of displays 185 configured to display received video streams. All the modules are interconnected via a dedicated infrastructure network 145 that is typically a local area network (LAN), for example a local area network based on Gigabit Ethernet.

Video manager system 150 may be a device containing a software module that makes it possible to configure, control, and manage the video surveillance system, for example via an administration interface. Such tasks are typically carried out by an administrator (e.g. administrator 190) who is in charge of configuring the overall video surveillance system. In particular, administrator 190 may use video manager system 150 to select a source encoder configuration for each source device of the video surveillance system. In the state of the art, it is the only means to configure the source video encoders.

The set of displays 185 may be used by operators (e.g. operators 191) to watch the video streams corresponding to the scenes shot by the cameras of the video surveillance system.

The auto-setting server 160 contains a module for setting automatically or almost automatically parameters of cameras 110. It is described in more detail by reference to Figure 2.

Administrator 190 may use the administration interface of video manager system 150 to set input parameters of the auto-setting algorithm described with reference to Figures 3 to 7, carried out in in auto-selling sewer 160.

Figure 2 is a schematic block diagram of a computing device for implementing embodiments of the invention. It may be embedded in auto-selling sewer 160 described with reference to Figure 1.

The computing device 200 comprises a communication bus connected to: -a central processing unit 210, such as a microprocessor, denoted CPU; -an I/O module 220 for receiving data from and sending data to external devices. In particular, it may be used to retrieve images from source devices; -a read only memory 230, denoted ROM, for storing computer programs for implementing embodiments; -a hard disk 240 denoted HD; -a random access memory 250, denoted RAM, for storing the executable code of the method of embodiments of the invention, in particular an auto-setting algorithm, as well as registers adapted to record variables and parameters; -a user interface 260, denoted Ul, used to configure input parameters of embodiments of the invention. As mentioned above, an administration user interface may be used by an administrator of the video surveillance system.

The executable code may be stored either in random access memory 250, in hard disk 240, or in a removable digital medium (not represented) such as a disk of a memory card.

The central processing unit 210 is adapted to control and direct the execution of the instructions or portions of software code of the program or programs according to embodiments of the invention, which instructions are stored in one of the aforementioned storage means. After powering on, CPU 210 may execute instructions from main RAM memory 250 relating to a software application after those instructions have been loaded, for example, from the program ROM 230 or hard disk 240.

Figure 3 is a block diagram illustrating an example of an auto-setting method making it possible to set automatically parameters of a source device, typically a camera, according to embodiments of the invention.

As illustrated, a first phase is a learning phase (reference 300). According to embodiments, it is performed before the installation of the considered camera, for example during the development of a software application for processing images. Preferably, the learning phase is not specific to a type of camera (i.e. it is advantageously generic). During this phase, a relation or a function is established between a quality value (relating to the result of the image processing) and all or most of the relevant variables that are needed to estimate such a processing result quality. These relevant variables may include image quality-dependent parameters and/or scene-dependent parameters. As described hereafter, this relation or function, denoted quality function, may depend on a type of the missions that can be handled by any camera.

An objective of the learning phase is to obtain a quality function which is able to state prima facie the quality of an image in the context of a particular mission, as a function of parameters which have an impact on the mission.

According to particular embodiments, the output of the learning phase is a quality function that may be expressed as follows: fq"dbilmissions)(image quality, scene) where, missions is a type of mission; image quality is a set of parameters that may comprise a blur value, a noise value, and a contrast value; and scene is a set of parameters that may comprise a target size and a target velocity.

Therefore, in particular embodiments, the output of the learning phase may be expressed as follows: fq"y(missions) (noise, blur, contrast, target size, target velocity) The quality function fwaidy may be a mathematical relation or an n-dimensional array associating a quality value with a set of n parameter values, e.g. values of noise, blur, contrast, target size, target velocity.

As denoted with reference 305, the type of mission to be handled by the camera may be chosen by a user (or an installer) during installation of the camera or later on. Likewise, a user may select a region of interest (ROI) corresponding to a portion of an image to be processed. As illustrated with the use of dotted lines, this step is optional.

As illustrated, after a user has selected a type of mission, the quality function obtained from the learning phase may be written as follows: fq"ty(image quality, scene) or, according to the given example: fquabry(noise, blur, contrast, target size, target velocity) Alternatively, the auto-setting algorithm may be configured for a particular type of mission and the whole captured scene may be considered.

A second phase (reference 310) is directed to calibration. This is typically carried out during installation of the camera and aims at measuring scene values from the actual scene according to the settings of the camera, as well as at obtaining parameter values depending on the camera settings. This may take from a few minutes to a few tens of minutes. As explained hereafter, in particular with reference to Figures 4 and 7, it makes it possible to determine quality processing values according to the actual scene and the current camera settings. According to embodiments, the calibration phase is run only once.

The outputs of this phase may comprise: scene values (for example target size and target velocity); image quality values (for example noise, blur, and contrast) that may be determined as a function of the camera settings (for example gain and shutter speed); and image metrics (for example luminance) that may be determined as a function of the camera settings (for example gain and shutter speed). They can be expressed as follows: scene-related parameters: target size target speed image quality: noise = f -noose_calibration(gain, shutter speed) blur = f * blur calibrabon(gain, shutter speed) contrast = f * contrast calibration(gain, shutter speed) image metrics: luminance = 4,",ina",_callfratio,dgain, shutter speed) The functions (f vnoise_calibration, b(ur calibration, fcontrast calibration, fluminance calibration) may be mathematical relations or 2-dimensional arrays associating values with sets of 2 parameter values (gain and shutter speed).

A third phase (reference 315) is directed to operation. It is performed during the operational use of the camera to improve its settings. It is preferably executed in a very short period of time, for example less than one second, and without perturbation for the camera, except for changing camera settings (i.e. it is a non-invasive phase). It is used to select suitable camera settings, preferably the most suitable camera settings.

To that end, data obtained during the calibration phase are used to calculate good settings, preferably the best settings, according to the quality function determined during the learning phase, in view of the current environmental conditions. Indeed, the environmental conditions, typically lighting, may be different from the environmental conditions corresponding to the calibration. Accordingly, the calibration data must be adjusted to fit the current environmental conditions. Next, the adjusted data are used to calculate the best settings. This may be an iterative process since the adjustments of the calibration data are more accurate when camera settings get closer to the optimal settings. Such an operation phase is preferably carried out each time a new change of camera settings is needed.

The output of the operation phase is a camera setting, for example a set of gain and shutter speed values.

During the operation phase a test may be performed to determine whether or not the items of information determined during the calibration phase make it possible to obtain accurate results. If the items of information determined during the calibration phase do not make it possible to obtain accurate results, some steps of the calibration phase may be carried out again, as discussed with reference to Figure 6c.

Learning phase Video surveillance cameras can be used in quite different contexts that is to say to conduct different "missions" or "tasks". For example, some cameras may be used to provide an overall view, making it possible to analyse wide areas, for example for crowd management or detection of intruders, while others may be used to provide detailed views, making it possible, for example to recognize faces or license plates. Depending on the type of mission, the constraints associated with the camera may be quite different. In particular, the impact of the noise, blur, and/or contrast is not the same depending on the mission. For example, the blur has generally a high impact on missions for which details are of importance, e.g. for face or license plate readability. In other cases, the noise may have more impact, for example when scenes are monitored continuously by humans (due to the higher eye strain experienced on noisy videos). As set forth above, an objective of the learning phase is to get a quality function which is able to state prima facie the quality of an image in the context of a particular type of missions, as a function of parameters which have an impact on the missions.

According to embodiments, such parameters may be the followings: the parameters which represent a quality of images provided by the camera, which depend on the camera settings. Such parameters may comprise the noise, the blur, and/or the contrast; and the parameters that are directed to the scene and the mission to be performed, referred to as scene-dependent parameters hereafter, their values being referred to as scene values. Their number and their nature depend on the type of missions. These parameters may comprise a size of targets and/or a velocity of the targets. The values of these parameters may be predetermined, may be determined by a user, or may be estimated, for example by image analysis. They do not have a direct impact on the image quality but play a role in how difficult it is to fulfil a mission. For example, the noise has more impact on smaller targets than on larger targets so the perceived quality of noisy images will be worse when targets are smaller.

Regarding the image quality, it has been observed that the noise, the blur, and the contrast are generally the most relevant parameters. Nevertheless, camera settings have an impact on other parameters that may be considered as representative of the image quality, for example on the depth-of-field and/or on or the white balance. However, due to hyperfocal settings in video surveillance systems, the depth of field is usually not very relevant. Likewise, the white balance is generally efficiently handled by the camera auto-mode. Accordingly and for the sake of clarity, the following description is based on the noise, the blur, and the contrast as image quality parameters. However, it must be understood that other parameters may be used. Regarding the scene-dependent parameters, it has been observed that the target size and the target velocity are generally the most relevant parameters. Therefore, for the sake of clarity, although other parameters may be used, the following description is based on these two parameters.

Accordingly, the quality function determined in the learning phase may generally be expressed as follows: fq,,afity(missions)Onoise, blur, contrast, target size, target velocity) or as a set of functions (one function per type of mission denoted mission<i>): fq,di,ty(noise, blur, contrast, target size, target velocity) for mission<i> or as a function corresponding to a predetermined type of mission for which a video surveillance system is to be used: fc,"dbanoise, blur, contrast, target size, target velocity) Such a function makes it possible, during the operation phase, to select efficient camera settings for the mission to be carried out, in view of the noise, blur, contrast, target velocity, and target size corresponding to the current camera settings (according to the results obtained during the calibration phase).

totality -3 vnoise Vbiur 'contrast where V.., Vbk,-, and Vcontrast represent values for the noise, blur, and contrast parameters, respectively.

The quality function f"allty makes it possible to determine a quality value as a function of general image characteristics such as the noise, blur, and contrast, and of scene characteristics such as target size, for a particular mission. However, this function cannot be used directly since it is not possible to determine a priori the noise, blur, and contrast since these parameters cannot be set on a camera.

Calibration phase The objective of the calibration phase is to measure in-situ, on the actual camera and the actual scene, all the data that are required to calculate a quality value from an function as determined during the learning phase.

Accordingly, the calibration phase comprises three objectives: determining or measuring the scene-dependent parameters, for example a target size and a target velocity; estimating functions to establish a link between each of the image quality parameters (for example the noise, blur, and contrast) and the camera settings (for example the gain (G) and the shutter speed (S)) as follows: noise = -f noise_calibration(Gi S), in short noise"dG,S) blur = f -blur calibration(G, contrast = -f contrast_calibration(G, 3), in short contrastaG,S) estimating a function to establish a link between an image metric (for example the luminance) and the camera settings (for example the gain (G) and the shutter speed (S)). According to embodiments, luminance is used during the operation phase to infer new calibration functions when scene lighting is modified. It may be expressed as follows: luminance = t nummance_calibratori(G, S), in short laG,S) Figure 4 is a block diagram illustrating a first example of steps carried out during a calibration phase of an auto-setting method as illustrated in Figure 3.

As illustrated, a first step (step 400) is directed to selecting camera settings.

According to embodiments, this step comprises exploring the manifold of all camera setting For the sake of illustration, this function may be scaled between 0 (very low quality) and 1 (very high quality).

According to embodiments, the quality function is set by an expert who determines how to penalize the noise, blur, and contrast for a considered type of mission.

For the sake of illustration, the quality function may be the following: X Vbiur X Vcon"."" in short blurcedG,S) values, for example all pairs of gain and shutter speed values, and selecting a set of representative pairs in order to reduce the number of camera settings to analyse.

For the sake of illustration, the shutter speed values to be used may be selected as follows: So = min(S) and S,+, = S, x 2 with index /varying from 0 to n so that S" max(S) and Snir/ > max(S) and where min(S) is the smallest shutter speed and max(S) is the highest shutter speed.

If shutter speeds the camera may accept are discrete values, the shutter speeds are selected so that their values are the closest to the ones selected according to the previous relation (corresponding to a logarithmic scale).

Similarly, the gain values to be used may be selected according to a uniform linear scale as follows: Go = min(G) and Gii./ is determined such that J(G1) I(Ci+1) l(G) '(Si) with index /varying from 0 to n such that G, max(G) and Gni./ > max(G) and where / is the luminance of the image, min(G) is the smallest gain, and max(G) is the higher gain.

As a consequence, the gain and shutter speed values have an equivalent scale in terms of impact on the luminance. In other words, if luminance of the image is increased by a value A when shutter speed value goes from one value to the next, gain value is selected such that the luminance is also increased by the value A when moving from the current gain value to the next one.

After having selected a set of gain and shutter speed values at step 400, images are obtained from the camera set to these values (step 405). For the sake of illustration, three to ten images may be obtained, preferably during a short period of time, for each pair (G, S) of gain and shutter speed values.

In order to optimize the time for obtaining these images and the stability of the camera during acquisition of the images, the change of camera settings is preferably minimized, i.e. the settings of the camera are preferably changed from one gain and/or shutter value to the next ones (since it takes a longer time for a camera to proceed to large changes in gain and shutter speed).

Therefore, according to embodiments, images are obtained as follows for each of the selected gain and shutter speed values: the gain is set to its minimum value (min(G)) and all the selected values of the shutter speed are set one after the other according to their ascending order (from min(S) to max(S)), a number of three to ten images being obtained for each pair of values (G, S); - the value of the gain is set to the next selected one and all the selected values of the shutter speed are set one after the other according to their descending order (from max(S) to min(S)), a number of three to ten images being obtained for each pair of values (G, S); and -these two previous steps are repeated with the next values of the gain until images have been obtained for all selected values of the gain and shutter speed.

Next, after having obtained images for all the selected values of the gain and shutter speed, an image metric is measured for all the obtained images (step 410), here the luminance, and an image quality analysis is performed for each of these images (step 415).

The measurement of the luminance aims at determining a relation between the luminance of an image and the camera settings used when obtaining this image, for example a gain and a shutter value. For each obtained image, the luminance is computed and associated with the corresponding gain and shutter speed values so as to determine the corresponding function or to build a 2-dimensional array wherein a luminance is associated with a pair of gain and shutter speed values (denoted S)). According to embodiments, the luminance corresponds to the mean of pixel values (i.e. intensity values) for each pixel of the image. According to embodiments, the entropy of the images is also computed during measurement of the luminance for making it possible to determine a contrast value during the image quality analysis. Like the luminance, the entropy is computed for each of the obtained images and associated with the corresponding gain and shutter speed values so as to determine the corresponding function or to build a 2-dimensional array wherein an entropy is associated with a pair of gain and shutter speed values (denoted EGai(G, S)). According to embodiments, measurement of the entropy comprises the steps of: determining the histogram of the image pixel values, for each channel (i.e. for each component), that is to say counting the number of pixels c, for each possible pixel value (for example for i varying from 0 to 255 if each component is coded with 8 bits); and computing the Shannon entropy according to the following relation: E = -EMslog2N, with n is the total number of pixels in all channels.

As described hereafter, the entropy may be determined as a function of the luminance (and not of the camera settings, e.g. gain and shutter speed). Such a relationship between the entropy and the luminance can be considered as valid for any environmental conditions (and not only the environmental conditions associated with the calibration).

Therefore, after having computed an entropy and a luminance for each of the obtained images, the entropy values are associated with the corresponding luminance values so as to determine the corresponding function or to build a 1-dimensional array wherein entropy is associated with luminance (denoted E(0).

Turning back to Figure 4 and as described above, the image quality analysis (step 415) aims at determining image quality parameter values, for example values of noise, blur, and contrast from the images obtained at step 405, in order to establish a relationship between each of these parameters and the camera settings used for obtaining the corresponding images.

During this step, a relationship between the contrast and the luminance is also established.

Noise values are measured for the obtained images and the measured values are associated with the corresponding gain and shutter speed values so as to determine the corresponding function or to build a 2-dimensional array wherein a noise value is associated with a pair of gain and shutter speed values (noiseca,(G, S)).

According to an embodiment, the noise of an image is determined as a function of a set of several images (obtained in a short period of time) corresponding to the same camera settings and as a result of the following steps: -removing the motion pixels, i.e. the pixels corresponding to objects in motion or in other words, removing the foreground; -computing a temporal variance for each pixel (i.e., the variance of the fluctuation of each pixel value over time, for each channel); and -computing a global noise value for the set of images as the mean value of the computed variances between all pixels and all channels.

The obtained values make it possible to establish a relationship between the noise and the camera settings.

Likewise, blur values are computed for the obtained images so as to establish a relationship between the blur and the camera settings.

According to embodiments, a blur value is determined as a function of a target velocity and of a shutter speed according to the following relation: blur = Illitarget11* shutter_speed where 'Oritry" is the target velocity, the blur value being given in pixels, the target velocity being given in pixels/second, and the shutter speed being given in seconds.

Therefore, in view of the environmental conditions associated with the calibration phase (denoted "calibration environmental conditions"), the blur may be determined as follows: bhu-c", (5) = Pt.",,11* s The target velocity may be predetermined, set by a user, or measured from a sequence of images as described hereafter.

The blur is computed for each of the obtained images according to this relation and the obtained values are associated with the corresponding shutter speed values (the gain does not affect the blur) so as to determine the corresponding function or to build a 1-dimensional array wherein a blur value is associated with shutter speed values (b/urcadS)).

Similarly, the contrast is computed for each of the obtained images It may be obtained from the entropy according to the following relation: 2 entropy contrast = 2. _entropy where, for example, max entropy is equal to 8 when the processed images are RGB images and each component is encoded over 8 bits.

Accordingly, the contrast contrast,IG, S) may be obtained from the entropy Eca,(G, S). In other words, contrast values may be expressed as a function of the gain and of the shutter speed values from the entropy expressed as a function of the gain and of the shutter speed values.

Likewise, the contrast contrast(l) expressed as a function of the luminance may be obtained from the entropy Ea) that is also expressed as a function of the luminance. This can be done as a result of the following steps: - measuring the entropy of each of the obtained images; - determining the relationships between the measured entropy values and the camera settings, for example the gain and the shutter speed, denoted Ecal(G, S); - obtaining the previously determined relationships between the luminance values and the camera settings, for example the gain and the shutter speed, denoted 1.1(G, S); discarding selected camera settings corresponding to gain values leading to noise values that exceed a predetermined noise threshold (the noise may have an impact on the entropy when the noise is too large and thus, by limiting noise to variance values below a predetermined threshold, for example 5 to 10, the impact is significantly reduced); gathering the remaining entropy values and luminance values, that are associated with gain and shutter speed values, to obtain a reduced data collection of entropy and luminance values sharing the same camera settings.

This data collection makes it possible to establish the relationships between entropy and luminance values, for example by using simple regression functions such as a linear interpolation on the entropy and luminance values; - determining the relationships between the contrast and the entropy as a function of the luminance, for example according to the following relation: 2E(1) contrast(J) = 2rnax entropy Turning back to Figure 4, it is illustrated how scene-dependent parameter values, for example target size and/or target velocity, may be obtained.

To that end, short sequences of consecutive images, also called chunks, are obtained. For the sake of illustration, ten to twenty chunks representative of the natural diversity of the targets are obtained.

According to particular embodiments, chunks are recorded by using the auto-mode (although the result is not perfect, the chunk analysis is robust to the blur and to the noise and thus, does not lead to significant errors). A motion detector of the camera can be used to detect motion and thus, to select chunks to be obtained.

The recording duration depends on the time it takes to get enough targets to reach statistical significance (10 to 20 targets is generally enough). Depending on the case, it can take only few minutes to several hours Of very few targets are spotted per hour).

In order to avoid waiting, it is possible to use chunk fetching instead of chunk recording (i.e. if the camera had already been used prior to the calibration step, the corresponding videos may be retrieved and used).

After being obtained, the chunks are analyzed to detect targets (step 425) to make it possible to estimate their size and optionally their velocity (step 430). This estimating step may comprise performing a statistical analysis of the values of the parameters of interest (e.g. target size, target velocity). Next, the mean, median, or any other suitable value extracted from the distribution of parameter values is computed and used as the value of reference.

The velocity of targets can be very accurately derived by tracking some points of interest of the target. By using this in combination with a background subtraction method (e.g. the known MOG or MOG2 method described, for example, in Zoran Zivkovic and Ferdinand van der Heijden, "Efficient adaptive density estimation per image pixel for the task of background subtraction". Pattern recognition letters, 27(7):773-780, 2006), it is possible to avoid the detection of the fixed points of interest from the background and thus, to determine velocity with high accuracy even with blurry targets. The target velocity is simply the main velocity of points of interest.

Figure 5 illustrates an example of the distribution of the target velocity (or, similarly, the distribution of the velocity of the points of interest). From such a representation, a target velocity value may be obtained. For the sake of illustration, it can be chosen so as to correspond to the mean velocity for given targets. For the sake of illustration, one can choose a value corresponding to the "median 80%", i.e. a velocity value such that 80% of velocities are under this value and 20% of velocities are over this value.

The target size can be obtained through methods as simple as background subtraction, or more sophisticated ones like target detection algorithms (e.g. face recognition, human detection, or license plate recognition), which are more directly related to the detection of the targets corresponding to the task. Deep learning methods are also very effective. Outliers can be removed by using consensus-derived methods, or by using combinations of background subtraction and target detection at the same time. However, since only statistical results are obtained, it does not matter if some errors exist with such algorithms, since the errors should be averaged out to zero. This tolerance to errors makes such methods robust.

Operation phase As described previously, the operation phase aims at improving camera settings, preferably at determining optimal (or near-optimal) camera settings for a current mission and current environmental conditions, without perturbing significantly the use of the camera. To that end, the operation phase is based on a prediction mechanism (and not on an exploration / measurement mechanism). It uses, in particular, the quality function (fgua Iv) determined in the learning phase, the relationships between image quality parameters and camera settings (e.g. noisecai(G, S), blurcadG, S), and contrastaG, S)) determined during the calibration phase, scene-dependent parameters also determined during the calibration phase, and image metrics relating to images obtained with the current camera settings.

Indeed, since the environmental conditions of the calibration phase and the current environmental conditions (i.e. during the operation phase) are not the same, the new relationships between image quality parameters and camera settings should be predicted so as to determine camera settings as a function of the quality function, without perturbing the camera.

According to embodiments, the noise may be predicted from the gain, independently from the shutter speed. Moreover, it is independent from lighting conditions. Therefore, the relationships between the noise and the gain for the current environmental conditions may be expressed as follows: noise.ent(G) = noisecai(G) wherein the noise value associated with a given gain value corresponds to the mean noise for this gain and all the shutter speed values associated with it.

If a noise value should be determined for a gain value that has not been selected during the calibration phase (i.e., if there is a gain value for which there is no corresponding noise value), a linear interpolation may be carried out.

Table 1 in the Appendix gives an example of the relationships between the noise and the gain.

Still according to embodiments, the blur may be determined as a function of the target velocity and the shutter speed as described above. It does not depend on lighting conditions. Accordingly, the relationships between the blur and the shutter speed for the current environmental conditions may be expressed as follows: blur"",t(S)= blureadS) Table 2 in the Appendix gives an example of the relationships between the blur and the shutter speed.

Still according to embodiments, prediction of the contrast as a function of the camera settings according to the current environmental conditions (denoted contrastc"rent(G, S)) comprises prediction of the luminance as a function of the camera settings for the current environmental conditions (denoted /"",,e(G, S) or /fred(G, S)) and the use of the relationships between the contrast and the luminance (contrast(/)) according to the following relation: contrastcurrent(G, S) = contrastagrendlcurrendG, S)) Prediction of the luminance as a function of the camera settings for the current environmental conditions (I",ent(G, S) or /pred(G, S)) may be based on the luminance expressed as a function of the camera settings for the calibration environmental conditions (noted lesi(G, S)) and on a so-called shutter shift method.

The latter is based on the assumption that there is a formal similarity between a change in lighting conditions and a change in shutter speed. Based on this assumption, the current luminance lea may be expressed as follows: fact = lcurrent(Gact Sac) = Icat(Gact, Sad + AS) where (Gad, Sact) is the current camera settings and As is a shutter speed variation.

Therefore, the relationship between the luminance and the camera settings for the current environmental conditions may be determined as follows: interpolating the computed luminance values loal(G, S) to obtain a continuous or pseudo-continuous function; for the current gain Gad, determining As so that led(Gad, Sad + AS) = 'act for example by using the inverse function of the luminance expressed as a function of the shutter speed (for the current gain Gad), i.e. the shutter speed expressed as a function of the luminance, and computing As as As = Sfiacd -Sact; and determining the whole function lcu"ent(G,S) by using the formula IG,,,,t(G, = losy(G, S + AS) However, if the assumption that there is a formal similarity between a change in lighting conditions and a change in shutter speed is correct in the vicinity of the current camera settings, it is not always true for distant camera settings. Accordingly, an iterative process may be used to determine the camera settings to be used, as described hereafter.

Table 3 in the Appendix gives an example of the relationships between the contrast and the gain and the shutter speed.

After having predicted the image quality parameters for the current environmental conditions, optimization of the current camera settings may be carried out. It may be based on a grid search algorithm according to the following steps: sampling the manifold of possible gain and shutter speed values to create a 2D grid of different (Gpmcf, Sore) pairs; for each of the (Gprech Soled) pairs, denoted (Gared), Spred), computing the values of the image quality parameters according to the previous predictions (noisecurrent(GPrea bkircuffent(SPred), and contra--Alcudentg(Gpredd Spredd))); for each (Gprecu, Spred) pair, computing a score as a function of the quality function determined during the learning phase, of the current mission (missionact), and of the computed values of the image quality parameters as follows: score,, = taaa",,,(inissionaad (noiseciarendGpred,), blurawreadSpread, and contrast (I(G _Guffaw, -predm;lea, target size, target velocity) where target size and target velocity values have been calculated during the calibration phase, - identifying the best score (or one of the best scores), i.e. max(scoret), to determine the camera settings to be used, i.e. (G"ext, Snen) = argmax(score"). Table 4 in the Appendix gives an example of the relationships between the score and the gain and the shutter speed.

In order to improve the accuracy of the camera settings, the latter may be determined on an iterative basis (in particular to take into account that the assumption that there is a formal similarity between a change in lighting conditions and a change in shutter speed is not always true for distant camera settings).

Accordingly, after the next camera settings have been determined, as described above, and set, the luminance corresponding to these next camera settings is predicted (/wed = icurrent(Gnext, Snext)), a new image corresponding to these camera settings is obtained, and the luminance of this image is computed. The predicted luminance and the computed luminance are compared.

If the difference between the predicted luminance and the computed luminance exceeds a threshold, for example a predetermined threshold, the process is repeated to determine new camera settings. The process may be repeated until the difference between the predicted luminance and the computed luminance is less than the threshold or until camera settings are stable.

It is to be noted that region of interests (ROls) may be taken into account for determining image quality parameter values (in such a case, the image quality parameter values are determined from the ROls only) and for optimizing camera settings.

Figure 6a illustrates a first example of steps for determining new camera settings during the operational use of a camera, without perturbing the use of the camera. This may correspond at least partially to step 315 in Figure 3.

As illustrated, first steps are directed to: obtaining images (step 600) from a camera set with current camera settings, from which an actual luminance (lad) may be computed, obtaining these camera settings (step 605), i.e. the actual gain and the shutter speed (Gad and Sad) in the given example, and -obtaining the relationships (step 615) between the contrast and the camera settings for the calibration environmental conditions (contrastea,(G, S)), between the contrast and the luminance (contrast(I)), and between the luminance and the camera settings for the calibration environmental conditions (loodG, S)).

Next, the relationships between the luminance and the camera settings for the current environmental conditions (1"ifoot(G, S)) and the relationship between the contrast and the camera settings for the current environmental conditions (contrasCro"t(G, S)) are predicted (step 620), for example using the method and formula described above.

In parallel, before, or after, the quality function Motility), the relationships between the noise and the camera settings for the calibration environmental conditions (noiseaG, S)), the relationships between the blur and the camera settings for the calibration environmental conditions (blurco,(G, S)), and the scene-dependent parameter values, e.g. the target size and preferably the target velocity, are obtained (step 625).

Next, these relationships as well as the relationships between the contrast and the camera settings for the current environmental conditions (contrasteoffoo((G, S)) are used to predict image quality parameter values for possible gain and shutter speed values (step 630).

As described above, these image quality parameter values may be computed for different (G prod, Spred) pairs forming a 2D grid.

These image quality parameter values are then used with the scene-dependent parameter values to compute scores according to the previously obtained quality function (step 635). According to embodiments, a score is computed for each of the predicted image quality parameter values.

Next, optimized camera settings are selected as a function of the obtained scores and the settings of the camera are modified accordingly (step 640).

According to embodiments, it is determined whether or not predetermined criteria are met (step 645), for example whether or not the actual luminance of an obtained image is close to the predicted luminance.

If the criteria are met, the process is stopped until a new optimization of the camera settings should be made. Otherwise, if the criteria are not met, new camera settings are estimated, as described above.

According to embodiments and as described above, prediction of the luminance as a function of the camera settings for the current environmental conditions Upred(G,S) or / * current(G, S)) may be based on the luminance expressed as a function of the camera settings for the calibration environmental conditions (1,31(G, S)) and computed according to the shutter shift method.

However, it has been observed that the accuracy of the results obtained according to these embodiments is increasingly better when current environmental conditions get closer to the calibration environmental conditions and that it decreases when current environmental conditions deviate from the calibration environmental conditions. This may lead to prediction errors, e.g. when trying at night to apply the results of a calibration performed at the brightest hours of a day for an outdoor camera.

Accordingly, it may be efficient to determine the relationships between the luminance and the camera settings for different calibration environmental conditions i (denoted ILI(G,S)), i varying, for example, from 0 to n.

In such a case, the relationships between the luminance and the camera settings to be used for the current environmental conditions may be selected from among all the relationships between the luminance and the camera settings determined during the calibration phase (110a4G, S)) so that: = argrninial act - 9act,Sact)l) In other words, the relationships associated with the calibration environmental conditions i are selected so as to minimize the gap between the measured luminance (/act) and the luminance (iciai(Gc,",Sa")) obtained in the same conditions (i.e. for same G and S as in the current situation).

Figure 6b illustrates a second example of steps for determining new camera settings during the operational use of a camera, without perturbing the use of the camera.

As illustrated, the steps are similar to those described with reference to Figure 6a except steps 615' and 620'.

According to the illustrated example, step 615' is similar to step 615 described with reference to Figure 6a except that several relationships between the luminance and the camera settings (i(G, S)) corresponding to different environmental conditions i are obtained.

In step 620', the relationships between the luminance and the camera settings corresponding to the calibration environmental conditions i that are the closest to the current environmental conditions are selected (i.e. i is determined) and the relationships between the luminance and the camera settings for the current environmental conditions(current(G, S)) and the relationship between the contrast and the camera settings for the current environmental conditions (contrast"rent(G, S)) are predicted, for example using the method and formula described above.

It has been observed that such a way of determining the relationships between the luminance and the camera settings provides accurate results as long as the current environmental conditions are not too far from the calibration environmental conditions. As a consequence, if the current environmental conditions are too far from the calibration environmental conditions, it may be appropriate to determine new relationships between the luminance and the camera settings.

Therefore, according to particular embodiments, the relationships between the luminance and the camera settings (1,1G, S)) for the current environmental conditions may be determined if the latter are too different from the calibration environmental conditions.

Indeed, the obtained relationships between the luminance and the camera settings should correspond to environmental conditions uniformly spanning the whole manifold of environment conditions. However, since there is no way of setting the environment conditions, it is not possible to obtain relationships between the luminance and the camera settings at will, for example during a complete calibration process. Accordingly, it may be useful to detect when environmental conditions are suitable for obtaining new relationships between the luminance and the camera settings and then, possibly, obtain these new relationships. This can be done during operational use of the camera.

Obtaining the relationships between the luminance and the camera settings may consist in carrying out steps 400, 405, and 410 (at least the step of measuring image metrics laal(G,S)) described with reference to Figure 4, for the current environmental conditions. According to a particular embodiment, detection of environmental conditions that should trigger obtaining relationships between the luminance and the camera settings for the current environmental conditions may be based on direct measurements of the current environmental conditions via a sensor, for example a light meter. By comparing the current output of the sensor (environment valueact) with its output(s) during the calibration phase (environment valuecana), one may determine whether or not the relationships between the luminance and the camera settings should be determined for the current environmental conditions. For example, if the difference between these outputs is greater than a predetermined threshold (lenvironment valueact -environment value calibra(ion' > threshold), the relationships between the luminance and the camera settings is determined for the current environmental conditions.

Still according to a particular embodiment, the environmental conditions may be determined indirectly through the images, by comparing the luminance value (lad) of a current image with the corresponding one associated with the calibration environmental conditions (i.e. the luminance associated with the corresponding camera settings ( icarorabon(Gach Sact))-Again, for ac the sake of illustration, if the difference between these values is greater than a predetermined threshold (I/ act -Ica libratiodG act, SJI > threshold), the relationships between the luminance and the camera settings is determined for the current environmental conditions.

Still according to a particular embodiment, triggering a step of obtaining the relationships between the luminance and the camera settings for the current environmental conditions is based on measuring an error prediction. This can be done by comparing the predicted luminance value yamd(Gaaa Sad) or /,,,,e(Gad, Sad)) with the luminance value (lad) of a current image. To that end, predicted luminance values are advantageously stored after setting new camera settings (e.g. step 640 in Figure 6a or 6b).

Still for the sake of illustration, if the difference between these values is greater than a predetermined threshold (Ilaat -/arad(Gad, Saadi > threshold), the relationships between the luminance and the camera settings is determined for the current environmental conditions.

Alternatively, the relationships between the luminance and the camera settings is determined for the current environmental conditions if p red (Gast act)-actl > threshold where /,"a, represents the luminance maximum possible value.

It is observed that the last embodiment is generally more efficient than the others in that it is based on a parameter (luminance prediction) that is to be optimized. Moreover, it does not require any additional sensor.

It is further observed that determining the relationships between the luminance and the camera settings is an invasive process for the camera since images from this camera are not usable for other purpose during such a step. It may take few minutes. For this reason, approval from the user is preferably requested before carrying out such a step.

Figure 6c illustrates another example of steps for determining new camera settings during the operational use of a camera, while perturbing as little as possible the use of the camera.

Steps 600 to 640 are similar to the corresponding steps described by reference to Figure 6b.

As illustrated, once camera settings have been modified, the camera is used for its purpose on a standard basis (step 650).

In parallel, a prediction error (PredE) is estimated (step 655). Such a prediction error is typically based on the predicted luminance value (4"eiGact Sad) or leuriont(Gact, Sad)) and the current luminance value (lad), as described above.

Next, this prediction error is compared to a threshold (0) (step 660). If the prediction error is greater than the threshold, it is preferably proposed to a user to measure the luminance for several camera settings so as to obtain new relationships between the luminance and the camera settings (InGai(G,S)) (step 665). As described above, this step is optional.

If it is determined that the luminance is to be measured for several camera settings according to the current environmental conditions (denoted n) for obtaining new relationships between the luminance and the camera settings (resi(G,S)), these steps are carried out (step 670). As mentioned above, this can be done by carrying out steps 400, 405, and 410 (at least the step of measuring image metrics tc,,,(G,S)) described in reference to Figure 4, for the current environmental conditions.

Then, the camera settings are determined and the settings of the camera are modified as described above, for example by reference to Figure 6b.

According to particular embodiments, the calibration data are associated with environmental conditions corresponding to a single given time (i.e. the calibration data are associated with a single given type of environmental conditions). In such a case, new calibration data corresponding to new environmental conditions are stored in lieu of the previous calibration data.

While the processes described above aim at optimizing camera settings on a request basis, for example upon request of a user, it is possible to control automatically the triggering of the process of auto-setting camera parameters. It is also possible to pre-determine camera settings so that as soon as conditions have changed significantly, new settings are applied instantaneously without calculations. Such an automatic process presents several advantages among which are: the whole operation phase is automated and can be run continuously without any user decision; the time needed to make changes of camera settings is much reduced between the decision to change and the change itself; and such an auto-setting-monitored system is able to react very quickly to a sudden change of environment conditions such as on/off lighting.

To that end, the current camera setting values and the luminance value should be obtained on a regular basis. The other steps of the operation phase remain basically the same since computations are based on these values and on values determined during the calibration phase.

According to particular embodiments, predicting image quality parameter values (e.g. steps 620 and 630 in Figure 6a), determining scores for camera settings (e.g. step 635 in Figure 6a), and enabling selection of camera settings are carried out in advance, for example at the end of the calibration phase, for all (or many) possible measurement values such as the gain, shutter speed and luminance (G, S, l).

This leads to a best camera setting function that gives optimized camera settings as a function of camera settings and luminance in view of the values obtained during the calibration phase. Such a best camera setting function may be expressed as follows: (Gnext, ST.<t) = best camera settings(G, S, I) To determine such a continuous function, a simple data regression or an interpolation may be used.

Operation phase mainly consists in measuring the current camera setting values and the luminance of the current image (Gad, Sad, lad) and determining optimized camera settings as a result of the best camera setting function determined during the calibration phase. If optimal determined camera setting values (Gne,d, Sne,d) are different from the current values (Geet, Sect), the camera settings are changed.

Figure 7 is a block diagram illustrating a second example of steps carried out during a calibration phase of an auto-setting method as illustrated in Figure 3.

The steps illustrated in Figure 7 differ as a whole from the those of Figure 4 in that they comprise steps of predicting image quality parameter values (step 700), of determining scores for camera settings and luminance values (step 705), and of determining a function for determining camera settings (step 710), for all possible camera setting values and for all possible luminance values (G, S, I).

Figures 8 and 9 are sequence diagrams illustrating an example of steps carried out during a calibration phase of an auto-setting method as illustrated in Figure 3.

Step 810 corresponds to the recording of images generated with different camera parameters, e.g., different values of gain and shutter, and comprises steps 811 to 817.

In step 811, controller 801 requests to camera 803 the minimal and maximal values of gain and shutter speed it supports. Upon reception of request 811, the camera transmits its upper and lower bounds of gain and shutter speed to the controller. Based on the obtained bounds of gain and shutter speed, the controller determines intermediate values of gain and shutter speed (step 813). An example of method for determining intermediate values of gain and shutter speed is described at step 400 in Figure 4. The different couples of (G, S) values form a manifold. In a variant, the camera transmits couples of (G, 5) values to the controller that selects at least a subset of the obtained couples of (G, S) values to form a manifold.

In step 814, the controller requests reception of a video stream to the camera.

Upon reception of request 814, the camera starts transmission of a video stream.

In step 816, the controller selects a couple of (G, S) values of the manifold and sets the gain and shutter speed parameters of the camera with the selected couple of values. The controller analyses the received stream and detects a modification of the image parameters. The analysis may be launched after a predetermined amount of time or when detecting that characteristics of the obtained images are rather fixed, since the modification of gain and shutter speed values may temporary lead to a generation of images with variable/changing characteristics. For a given couple of (G, S) values, N images are recorded and stored in controller memory 802 (step 817). The recording of N images (with N>1) is useful for computing noise.

Steps 816 and 817 are applied for each couple (G, S) of the manifold determined at step 813. Steps 816 and 817 are similar to step 405 in Figure 4.

Step 820 is an analysis of the stored images, and comprises steps 821, 822 and 823.

In step 821, the controller retrieves, for a given couple of values (G, S) the associated images stored in the controller memory, and an image metric is measured for all the obtained images (e.g., the luminance) (step 822). The measurement of the luminance aims at determining a relation between the luminance of an image and the camera settings used when obtaining this image, for example a gain and a shutter speed value. For each obtained image, the luminance is computed and associated with the corresponding gain and shutter speed values so as to determine the corresponding function or to build a 2-dimensional array wherein a luminance is associated with a pair of gain and shutter speed values (denoted lea,(G, S)). According to embodiments, the luminance corresponds to the mean of pixel values (i.e. intensity values) of the image.

According to embodiments, the entropy of the images is also computed during measurement of the luminance for making it possible to determine a contrast value during the image quality analysis. Like the luminance, the entropy is computed for each of the obtained images and associated with the corresponding gain and shutter speed values so as to determine the corresponding function or to build a 2-dimensional array wherein an entropy is associated with a pair of gain and shutter speed values (denoted Eca,(G, S)).

According to embodiments, image quality parameter values are also computed, for example values of noise from the images obtained at step 821, in order to establish a relationship between each of these parameters and the camera settings used for obtaining the corresponding images (similarly to step 415 in Figure 4).

Then, the image metrics (e.g., luminance and entropy values) and the image quality parameter values of the given (G, S) values are stored in the controller memory (step 823). Steps 821 to 823 are applied to each couple (G, 5) of values of the manifold.

Figure 9 is a sequence diagram illustrating an example of steps carried out during a calibration phase of an auto-setting method as illustrated in Figure 3, and may be applied following the method of Figure 8.

Step 910 is a chunk retrieval method, and comprises steps 911 to 915.

In step 911, recording server 904 requests a video stream to the camera. Upon reception of request 911, the video stream is transmitted to the recording server (step 912). The recording server may apply basic image analysis technics, such as image motion detection, and stores the relevant parts of the video streams (named "chunks"), e.g. parts of video streams with moving targets.

In step 913, controller 801 requests chunks to the recording server. Upon reception of request 913, the recording server transmits chunks previously stored to the controller. This step is similar to step 420 in Figure 4.

In a variant, the camera may apply basic image analysis technics, and at step 911', the controller directly requests chunks to the camera. Upon reception of request 911' from the controller, the camera transmits chunks to the controller.

In step 915, chunks are selected and analysed (step 920) by applying computer vision-based technics (step 921), thereby determining scene-dependent parameters values (i.e., related to target size and optionally to target velocity). This step is similar to step 430 in Figure 4 scene-dependent parameters values are stored in the Finally, the determined controller memory (step 922).

Figure 10 is a sequence diagram illustrating an example of steps carried out during an operation phase of an auto-setting method as illustrated in Figure 3.

In step 1011, the controller requests an image to the camera. Upon reception of request 1011, the camera transmits an image to the controller. Then, the controller determines the current luminance value /ad of the obtained image.

In step 1014, the controller requests to the camera its current camera settings (Gad, /ad), which are transmitted to the controller at step 1015. These steps are similar to steps 600 and 605 in Figures 6a and 6b.

In step 1016, the controller obtains the relationships between the contrast and the camera settings for the calibration environmental conditions (contrastaG, S)), between the contrast and the luminance (contrast(0), and between the luminance and the camera settings for the calibration environmental conditions (las,(G, S)). In parallel, before, or after, the quality function (fquality, 1, the relationships between the noise and the camera settings for the calibration y environmental conditions (noiseaal(G, S)), the relationships between the blur and the camera settings for the calibration environmental conditions (blureal(G, S)), and the scene-dependent parameter values, e.g. the target size and preferably the target velocity, are obtained. This step is similar to steps 615 and 625 in Figure 6a.

At step 1017, based on the relationships obtained at step 1016, a couple (Gbest.Sbest) of "best" values are determined, and optionally, at step 1018, it is determined if it is different from the current camera settings (Gad, /ad). If true, the controller sets the camera parameters with the "best" values (step 1019).

In a variant, at step 1014, the controller requests the current camera settings (Gact, lad) to the controller memory, which are transmitted to the controller at step 1015. Then, steps 1016 to 1019 are applied, and the couple (Gbast,Sbasi) of "best" values is stored in the controller memory.

While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive, the invention being not restricted to the disclosed embodiment.

Other variations on the disclosed embodiment can be understood and performed by those skilled in the art, in carrying out the claimed invention, from a study of the drawings, the disclosure and the appended claims.

Such variations may derive, in particular, from combining embodiments as set forth in the summary of the invention and/or in the appended claims.

In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or other unit may fulfil the functions of several items recited in the claims. The mere fact that different features are recited in mutually different dependent claims does not indicate that a combination of these features cannot be advantageously used. Any reference signs in the claims should not be construed as limiting the scope of the invention.

APPENDIX

Gain Go Gi Noise noise...4GO noisenuifen,(G,) noise aq") nOiSecunent(20 current, -, Table 1: relationships between the noise and the gain Shutter speed So S1 Su Blur Blur b/urcurrendSo) blUrcurrendS1) blUrcurrent(S2) blurcurrendSn) Table 2relationships between the blur and the shutter speed Shutter speed/ Gain So S S2 Su Go contrast contrast=m4G0, 5,) -a; contrastnufferiGo, S,) *current(Go, current, -contrast 6, So) 52) G contrast.e4G,, So) contrastcrendGi, 51) contrast 001(G1, -1, contraste",,,t(Gb Sn) S2) G2 contrastcurentl a, So) con-rastc ae(G2, Si) co fra-)curent('r 2, 52) contrasta,(G2, Sn) contrast..4G,,, So) contrasta,crendG,,, St) contraStcurrendGn, S2) contrasto",,,t(G", Sn) Table 3: relationships between the contrast and the gain and the shutter speed Shutter speed/ Gain So S S2 Go score(Go, So) score(Go, St) score(Go, .52) score(Go, S") G score(Gi, So) score(Gi, Si) score(Gi, .52) score(Gi, Sn) G2 score(G2, So) score(G2, St) score(G2, S2) score(G2, Si') Gn score(Gn, So) score(Gn, Si) score(Gn, S2) I scoreGn, Sn) Table 4: relationships between the score and the gain and the shutter speed

Claims

CLAIMS1. A method for improving settings of a camera, the method comprising: obtaining a first set of image characteristic values (Ica) of images captured by the camera at a first lighting condition, the image characteristics being dependent on the camera parameters (G, S), at least two image characteristic values of the first set respectively corresponding to at least two different values of a same camera parameter; determining at least one second set of image characteristic values (7 cur(ent) by adapting values of the obtained first set of image characteristic values from images captured by the camera at at least one second lighting condition, at least two image characteristic values of the second set corresponding respectively to at least two different values of a same camera parameter; selecting camera parameter values (G, S) for the camera, based on image characteristic values (contrastawrent) determined as a function of at least the second set of image characteristics (/6",,,"0; and modifying settings of the camera as a function of the selected camera parameter values, the method further comprising a step of comparing the first and the second lighting condition and a step of obtaining a third set of image characteristic values (foal) of images captured by the camera at a third lighting condition, at least two image characteristic values of the third set respectively corresponding to the said same camera parameter, the third set of image characteristic values being used in lieu of the first set as a function of the comparison step.
2. The method of claim 1, wherein the step of comparing the first and the second lighting condition comprises a step of comparing values obtained from a light sensor.
3. The method of claim 1, wherein the step of comparing the first and the second lighting condition comprises a step of comparing a luminance value ('act) of a current image with a luminance value (1,e,(Gect, Sect)) obtained from images captured by the camera at the first lighting condition with camera settings corresponding to camera settings (Gect, Sect) used to obtain the current image.
4. The method of claim 1, wherein the step of comparing the first and the second lighting condition comprises a step of comparing a luminance value (/ace) of a current image with a luminance value (/0-od(G3t, Sad)) predicted from luminance values obtained from images captured by the camera at the first lighting condition, the prediction being based on the luminance value ('act) of the current image, on camera settings corresponding to camera settings (Gact, Sact) used to obtain the current image, and on luminance values ('ta) and corresponding camera settings (Gee!, Stai) used to obtain the images captured by the camera at the first lighting condition.
5. The method of claim 4, wherein the prediction is based on a shutter shift method.
6. The method of any one of claims 1 to 5, wherein the step of selecting camera parameter values comprises a step of determining a quality value for each of at least two image characteristic values of the second set respectively corresponding to the at least two different values of the same camera parameter, the quality values being determined as a result of a predetermined function based on image characteristics.
7. The method of claim 6, wherein quality values are precomputed as a function of possible values of image characteristics.
8. The method of any one of claims 1 to 7, further comprising a step of determining the third set of image characteristic values, the step of determining the third set of image characteristic values comprising a step of determining relationships between lighting conditions (64) and values of at least one camera parameter (G, S) from images captured by the camera at the third lighting condition.
9. The method of claim 8, further comprising a step of obtaining an approval from a user before determining the third set of image characteristic values.
10. The method of any one of claims 1 to 9, wherein the image characteristics comprise noise, blur, and/or contrast and wherein the camera parameters comprise a gain and/or a shutter speed.
11. A computer program product for a programmable apparatus, the computer program product comprising instructions for carrying out each step of the method according to any one of claims 1 to 10 when the program is loaded and executed by a programmable apparatus.
12. A non-transitory computer-readable storage medium storing instructions of a computer program for implementing the method according to any one of claims 1 to 10.
13. A device for improving settings of a camera, the device comprising a microprocessor configured for carrying out the steps of: obtaining a first set of image characteristic values (lam) of images captured by the camera at a first lighting condition, the image characteristics being dependent on the camera parameters (G, S), at least two image characteristic values of the first set respectively corresponding to at least two different values of a same camera parameter; determining at least one second set of image characteristic values (4),",e") by adapting values of the obtained first set of image characteristic values from images captured by the camera at at least one second lighting condition, at least two image characteristic values of the second set corresponding respectively to at least two different values of a same camera parameter; selecting camera parameter values (G, S) for the camera, based on image characteristic values (contra stew-rent) determined as a function of at least the second set of image characteristics (/,""",.); and modifying settings of the camera as a function of the selected camera parameter values, the microprocessor being further configured for carrying out a step of comparing the first and the second lighting condition and a step of obtaining a third set of image characteristic values (//caa of images captured by the camera at a third lighting condition, at least two image characteristic values of the third set respectively corresponding to the said same camera parameter, the third set of image characteristic values being used in lieu of the first set as a function of the comparison step.
14. The device of claim 13, wherein the microprocessor is further configured so that comparing the first and the second lighting condition comprises comparing values obtained from a light sensor.
15. The device of claim 13, wherein the microprocessor is further configured so that comparing the first and the second lighting condition comprises comparing a luminance value (/,,,t) of a current image with a luminance value (laal(Gaat, Saat)) obtained from images captured by the camera at the first lighting condition with camera settings corresponding to camera settings (Gata Sad) used to obtain the current image.
16. The device of claim 13, wherein the microprocessor is further configured so that comparing the first and the second lighting condition comprises comparing a luminance value (fact) of a current image with a luminance value (4;,,d(Gact, Sat)) predicted from luminance values obtained from images captured by the camera at the first lighting condition, the prediction being based on the luminance value (fact) of the current image, on camera settings corresponding to camera settings (Gad, Sad) used to obtain the current image, and on luminance values ('cat) and corresponding camera settings (Gcai, Scat) used to obtain the images captured by the camera at the first lighting condition.
17. The device of any one of claims 13 to 16, wherein the microprocessor is further configured so that selecting camera parameter values comprises determining a quality value for each of at least two image characteristic values of the second set respectively corresponding to the at least two different values of the same camera parameter, the quality values being determined as a result of a predetermined function based on image characteristics.
18. The device of any one of claims 13 to 17, wherein the microprocessor is further configured for carrying out a step of determining the third set of image characteristic values, determining the third set of image characteristic values comprising determining relationships between lighting conditions (1,a) and values of at least one camera parameter (G, S) from images captured by the camera at the third lighting condition.
19. The device of claim 18, wherein the microprocessor is further configured for carrying out a step of obtaining an approval from a user before determining the third set of image characteristic values.
20. The device of any one of claims 13 to 19, wherein the image characteristics comprise noise, blur, and/or contrast and wherein the camera parameters comprise a gain and/or a shutter speed.