CN116468751A - High-speed dynamic image detection method and device - Google Patents
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Abstract
The invention discloses a high-speed dynamic image detection method and device. Wherein the method comprises the following steps: collecting original image data; dividing the original image data according to a target speed parameter to obtain divided image data; inputting the segmented image data into an identification matrix to obtain a first foreground target; and identifying the first foreground object according to the dynamic identification model to obtain a second foreground object. The invention solves the technical problems that in the image detection method in the prior art, only when a low-speed or high-speed moving object is monitored, the camera is used for locking a still frame, the original image is directly subjected to image content identification, the monitoring data of the high-speed moving object is generated according to the judgment result, the monitoring analysis task can not be completed under the condition that a plurality of moving objects or the high-speed moving object has high processing requirements, and the overall efficiency and accuracy of image detection are reduced.
Description
Technical Field
The invention relates to the field of camera image recognition and detection, in particular to a high-speed dynamic image detection method and device.
Background
Along with the continuous development of intelligent science and technology, intelligent equipment is increasingly used in life, work and study of people, and the quality of life of people is improved and the learning and working efficiency of people is increased by using intelligent science and technology means.
At present, dynamic image detection is widely applied in the fields of artificial intelligence, image processing and the like. At present, the related art has made great progress, but in a high-speed scene, the detection efficiency still needs to be improved, for example, in the fields of mobile robots, automatic driving and the like, dynamic image detection needs to be performed in the high-speed scene to ensure the driving safety and the accuracy of intelligent application. In the image detection method in the prior art, only when a low-speed or high-speed moving object is monitored, the camera is used for locking a still frame, image content identification is directly carried out on an original image, monitoring data of a high-speed moving object are generated according to a judging result, a monitoring analysis task cannot be completed under the condition that a plurality of moving objects or the high-speed moving object are very high in processing requirement, and the overall efficiency and accuracy of image detection are reduced.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a high-speed dynamic image detection method and a device, which at least solve the technical problems that in the prior art, the image content of an original image is directly identified by utilizing a mode that a camera locks a static frame only when an image detection method of a low-speed or high-speed moving object is used for monitoring, and monitoring data of a high-speed moving object is generated according to a judgment result, so that a monitoring analysis task cannot be completed under the condition that a plurality of moving objects or the high-speed moving object has high processing requirements, and the overall efficiency and accuracy of image detection are reduced.
According to an aspect of an embodiment of the present invention, there is provided a high-speed dynamic image detection method including: collecting original image data; dividing the original image data according to a target speed parameter to obtain divided image data; inputting the segmented image data into an identification matrix to obtain a first foreground target; and identifying the first foreground object according to the dynamic identification model to obtain a second foreground object.
Optionally, after the acquiring the raw image data, the method further comprises: and carrying out minimum binarization processing on the original image data.
Optionally, the segmenting the image data to be detected according to the target speed parameter, to obtain segmented image data includes: extracting motion parameters in the original image data; calculating a target speed parameter of the original image data according to the motion parameter; matching the target speed parameter with a preset speed threshold matrix to obtain an image segmentation strategy; and dividing the original image data according to the image division strategy to obtain the divided image data.
Optionally, the inputting the segmented image data into the recognition matrix to obtain the first foreground object includes: matching and screening the segmented image by using the identification matrix to obtain the first foreground object, wherein the first foreground object comprises at least one moving object, and the identification matrix is
Where P1 Pn are a number of segmented image data and M1 through Mn are a number of matching image data (i.e., first foreground objects).
According to another aspect of the embodiment of the present invention, there is also provided a high-speed dynamic image detection apparatus including: the acquisition module is used for acquiring original image data; the segmentation module is used for segmenting the original image data according to the target speed parameter to obtain segmented image data; the input module is used for inputting the segmented image data into the recognition matrix to obtain a first foreground target; and the identification module is used for identifying the first foreground object according to the dynamic identification model to obtain a second foreground object.
Optionally, the apparatus further includes: and the processing module is used for carrying out minimum binarization processing on the original image data.
Optionally, the segmentation module includes: an extracting unit for extracting motion parameters in the original image data; a calculation unit for calculating a target speed parameter of the original image data according to the motion parameter; the matching unit is used for matching the target speed parameter with a preset speed threshold matrix to obtain an image segmentation strategy; and the segmentation unit is used for segmenting the original image data according to the image segmentation strategy to obtain the segmented image data.
Optionally, the input module includes: the screening unit is used for matching and screening the segmented image by utilizing the identification matrix to obtain the first foreground object, wherein the first foreground object comprises at least one moving object, and the identification matrix is
Where P1 Pn are a number of segmented image data and M1 through Mn are a number of matching image data (i.e., first foreground objects).
According to another aspect of the embodiments of the present invention, there is also provided a nonvolatile storage medium including a stored program, where the program when executed controls a device in which the nonvolatile storage medium is located to perform a high-speed dynamic image detection method.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including a processor and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute a high-speed dynamic image detection method when executed.
In the embodiment of the invention, the acquisition of original image data is adopted; dividing the original image data according to a target speed parameter to obtain divided image data; inputting the segmented image data into an identification matrix to obtain a first foreground target; the method for identifying the first foreground object and obtaining the second foreground object according to the dynamic identification model solves the technical problems that in the image detection method in the prior art, only when a low-speed or high-speed moving object is monitored, the camera is used for locking a static frame, image content identification is directly carried out on an original image, monitoring data of the high-speed moving object are generated according to a judging result, a monitoring analysis task cannot be completed under the condition that a plurality of moving objects or the high-speed moving object has high processing requirements, and the overall efficiency and accuracy of image detection are reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a high-speed dynamic image detection method according to an embodiment of the present invention;
fig. 2 is a block diagram of a high-speed dynamic image detection apparatus according to an embodiment of the present invention;
fig. 3 is a block diagram of a terminal device for performing the method according to the invention according to an embodiment of the invention;
fig. 4 is a memory unit for holding or carrying program code for implementing a method according to the invention, according to an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present invention, there is provided a method embodiment of a high-speed dynamic image detection method, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that herein.
Example 1
Fig. 1 is a flowchart of a high-speed dynamic image detection method according to an embodiment of the present invention, as shown in fig. 1, the method comprising the steps of:
step S102, collecting original image data.
Specifically, in order to solve the technical problems that in the image detection method in the prior art, only when a low-speed or high-speed moving object is monitored, the original image is directly subjected to image content identification by utilizing a mode that a camera locks a still frame, and monitoring data of a high-speed moving object is generated according to a judging result, so that a monitoring analysis task cannot be completed under the condition that a plurality of moving objects or the high-speed moving object has high processing requirements, the overall efficiency and accuracy of image detection are reduced, and the original image data are required to be acquired firstly, wherein the original image data comprise high-speed dynamic video data or frame locking static picture data, and are arranged on scenes needing to capture the moving objects such as sports fields and outdoor fields through a high-precision camera array.
Optionally, after the acquiring the raw image data, the method further comprises: and carrying out minimum binarization processing on the original image data.
Specifically, after the original image data is obtained through the high-precision image capturing device, in order to perform better target recognition and judgment subsequently, the original image data needs to be subjected to minimum binarization processing to achieve the technical effects of optimizing the image data and increasing the image recognition precision, and it is noted that binarization is the simplest method for image segmentation, and can convert a gray image into a binary image, set a pixel gray greater than a certain critical gray value as a gray maximum value, and set a pixel gray smaller than the value as a gray minimum value, so that binarization is achieved. According to different threshold selection, the binarization algorithm is divided into a fixed threshold and an adaptive threshold. The more commonly used binarization method is: bimodal, P-parametric, iterative, OTSU, etc.
Step S104, dividing the original image data according to the target speed parameter to obtain divided image data.
Optionally, the segmenting the image data to be detected according to the target speed parameter, to obtain segmented image data includes: extracting motion parameters in the original image data; calculating a target speed parameter of the original image data according to the motion parameter; matching the target speed parameter with a preset speed threshold matrix to obtain an image segmentation strategy; and dividing the original image data according to the image division strategy to obtain the divided image data.
Step S106, inputting the segmented image data into an identification matrix to obtain a first foreground object.
Optionally, the inputting the segmented image data into the recognition matrix to obtain the first foreground object includes: matching and screening the segmented image by using the identification matrix to obtain the first foreground object, wherein the first foreground object comprises at least one moving object, and the identification matrix is
Where P1 Pn are a number of segmented image data and M1 through Mn are a number of matching image data (i.e., first foreground objects).
Specifically, when the identification matrix is constructed, matching construction is required according to the segmented image data and the matched image data, and the constructed matrix is used as an input/output matching matrix, if the segmented image data is input into the identification matrix, the first foreground object is obtained by: matching and screening the segmented image by using the identification matrix to obtain the first foreground object, wherein the first foreground object comprises at least one moving object, and the identification matrix isWhere P1 Pn are a number of segmented image data and M1 through Mn are a number of matching image data (i.e., first foreground objects).
Step S108, the first foreground object is identified according to the dynamic identification model, and a second foreground object is obtained.
Specifically, in the embodiment of the invention, after the first foreground object is generated, the recognition model needs to be trained through historical data for further recognition and screening, after the dynamic recognition model is perfected, the image data of the screened first foreground object is input into the dynamic recognition model, and the condition of the second foreground object is finally determined according to the result output by the model.
By the embodiment, the technical problems that in the image detection method in the prior art, only when a low-speed or high-speed moving object is monitored, the camera is used for locking a still frame, image content identification is directly carried out on an original image, monitoring data of a high-speed moving object are generated according to a judging result, a monitoring analysis task cannot be completed under the condition that a plurality of moving objects or the high-speed moving object has high processing requirements, and the overall efficiency and accuracy of image detection are reduced are solved.
Example two
Fig. 2 is a block diagram of a high-speed dynamic image detection apparatus according to an embodiment of the present invention, as shown in fig. 2, the apparatus including:
and an acquisition module 20 for acquiring the original image data.
Specifically, in order to solve the technical problems that in the image detection method in the prior art, only when a low-speed or high-speed moving object is monitored, the original image is directly subjected to image content identification by utilizing a mode that a camera locks a still frame, and monitoring data of a high-speed moving object is generated according to a judging result, so that a monitoring analysis task cannot be completed under the condition that a plurality of moving objects or the high-speed moving object has high processing requirements, the overall efficiency and accuracy of image detection are reduced, and the original image data are required to be acquired firstly, wherein the original image data comprise high-speed dynamic video data or frame locking static picture data, and are arranged on scenes needing to capture the moving objects such as sports fields and outdoor fields through a high-precision camera array.
Optionally, the apparatus further includes: and the processing module is used for carrying out minimum binarization processing on the original image data.
Specifically, after the original image data is obtained through the high-precision image capturing device, in order to perform better target recognition and judgment subsequently, the original image data needs to be subjected to minimum binarization processing to achieve the technical effects of optimizing the image data and increasing the image recognition precision, and it is noted that binarization is the simplest method for image segmentation, and can convert a gray image into a binary image, set a pixel gray greater than a certain critical gray value as a gray maximum value, and set a pixel gray smaller than the value as a gray minimum value, so that binarization is achieved. According to different threshold selection, the binarization algorithm is divided into a fixed threshold and an adaptive threshold. The more commonly used binarization method is: bimodal, P-parametric, iterative, OTSU, etc.
The segmentation module 22 is configured to segment the original image data according to a target speed parameter, so as to obtain segmented image data.
Optionally, the segmentation module includes: an extracting unit for extracting motion parameters in the original image data; a calculation unit for calculating a target speed parameter of the original image data according to the motion parameter; the matching unit is used for matching the target speed parameter with a preset speed threshold matrix to obtain an image segmentation strategy; and the segmentation unit is used for segmenting the original image data according to the image segmentation strategy to obtain the segmented image data.
The input module 24 is configured to input the segmented image data to the recognition matrix to obtain a first foreground object.
Optionally, the input module includes: the screening unit is used for matching and screening the segmented image by utilizing the identification matrix to obtain the first foreground object, wherein the first foreground object comprises at least one moving object, and the identification matrix is
Where P1 Pn are a number of segmented image data and M1 through Mn are a number of matching image data (i.e., first foreground objects).
The identifying module 26 is configured to identify the first foreground object according to a dynamic identifying model, so as to obtain a second foreground object.
Specifically, in the embodiment of the invention, after the first foreground object is generated, the recognition model needs to be trained through historical data for further recognition and screening, after the dynamic recognition model is perfected, the image data of the screened first foreground object is input into the dynamic recognition model, and the condition of the second foreground object is finally determined according to the result output by the model.
By the embodiment, the technical problems that in the image detection method in the prior art, only when a low-speed or high-speed moving object is monitored, the camera is used for locking a still frame, image content identification is directly carried out on an original image, monitoring data of a high-speed moving object are generated according to a judging result, a monitoring analysis task cannot be completed under the condition that a plurality of moving objects or the high-speed moving object has high processing requirements, and the overall efficiency and accuracy of image detection are reduced are solved.
According to another aspect of the embodiments of the present invention, there is also provided a nonvolatile storage medium including a stored program, where the program when executed controls a device in which the nonvolatile storage medium is located to perform a high-speed dynamic image detection method.
Specifically, the method comprises the following steps: collecting original image data; dividing the original image data according to a target speed parameter to obtain divided image data; inputting the segmented image data into an identification matrix to obtain a first foreground target; and identifying the first foreground object according to the dynamic identification model to obtain a second foreground object. Optionally, after the acquiring the raw image data, the method further comprises: and carrying out minimum binarization processing on the original image data. Optionally, the segmenting the image data to be detected according to the target speed parameter, to obtain segmented image data includes: extracting motion parameters in the original image data; calculating a target speed parameter of the original image data according to the motion parameter; matching the target speed parameter with a preset speed threshold matrix to obtain an image segmentation strategy; and dividing the original image data according to the image division strategy to obtain the divided image data. Optionally, the inputting the segmented image data into the recognition matrix to obtain the first foreground object includes: matching and screening the segmented image by using the identification matrix to obtain the first foreground object, wherein the first foreground object comprises at least one moving object, and the identification matrix isWherein P1-Pn are a plurality of partition chartsImage data, M1 through Mn, are several matching image data (i.e., first foreground objects).
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including a processor and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute a high-speed dynamic image detection method when executed.
Specifically, the method comprises the following steps: collecting original image data; dividing the original image data according to a target speed parameter to obtain divided image data; inputting the segmented image data into an identification matrix to obtain a first foreground target; and identifying the first foreground object according to the dynamic identification model to obtain a second foreground object. Optionally, after the acquiring the raw image data, the method further comprises: and carrying out minimum binarization processing on the original image data. Optionally, the segmenting the image data to be detected according to the target speed parameter, to obtain segmented image data includes: extracting motion parameters in the original image data; calculating a target speed parameter of the original image data according to the motion parameter; matching the target speed parameter with a preset speed threshold matrix to obtain an image segmentation strategy; and dividing the original image data according to the image division strategy to obtain the divided image data. Optionally, the inputting the segmented image data into the recognition matrix to obtain the first foreground object includes: matching and screening the segmented image by using the identification matrix to obtain the first foreground object, wherein the first foreground object comprises at least one moving object, and the identification matrix isWhere P1 Pn are a number of segmented image data and M1 through Mn are a number of matching image data (i.e., first foreground objects).
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, fig. 3 is a schematic hardware structure of a terminal device according to an embodiment of the present application. As shown in fig. 3, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to enable communication connections between the elements. The memory 33 may comprise a high-speed RAM memory or may further comprise a non-volatile memory NVM, such as at least one magnetic disk memory, in which various programs may be stored for performing various processing functions and implementing the method steps of the present embodiment.
Alternatively, the processor 31 may be implemented as, for example, a central processing unit (Central Processing Unit, abbreviated as CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and the processor 31 is coupled to the input device 30 and the output device 32 through wired or wireless connections.
Alternatively, the input device 30 may include a variety of input devices, for example, may include at least one of a user-oriented user interface, a device-oriented device interface, a programmable interface of software, a camera, and a sensor. Optionally, the device interface facing the device may be a wired interface for data transmission between devices, or may be a hardware insertion interface (such as a USB interface, a serial port, etc.) for data transmission between devices; alternatively, the user-oriented user interface may be, for example, a user-oriented control key, a voice input device for receiving voice input, and a touch-sensitive device (e.g., a touch screen, a touch pad, etc. having touch-sensitive functionality) for receiving user touch input by a user; optionally, the programmable interface of the software may be, for example, an entry for a user to edit or modify a program, for example, an input pin interface or an input interface of a chip, etc.; optionally, the transceiver may be a radio frequency transceiver chip, a baseband processing chip, a transceiver antenna, etc. with a communication function. An audio input device such as a microphone may receive voice data. The output device 32 may include a display, audio, or the like.
In this embodiment, the processor of the terminal device may include functions for executing each module of the data processing apparatus in each device, and specific functions and technical effects may be referred to the above embodiments and are not described herein again.
Fig. 4 is a schematic hardware structure of a terminal device according to another embodiment of the present application. Fig. 4 is a specific embodiment of the implementation of fig. 3. As shown in fig. 4, the terminal device of the present embodiment includes a processor 41 and a memory 42.
The processor 41 executes the computer program code stored in the memory 42 to implement the methods of the above-described embodiments.
The memory 42 is configured to store various types of data to support operation at the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, pictures, video, etc. The memory 42 may include a random access memory (random access memory, simply referred to as RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Optionally, a processor 41 is provided in the processing assembly 40. The terminal device may further include: a communication component 43, a power supply component 44, a multimedia component 45, an audio component 46, an input/output interface 47 and/or a sensor component 48. The components and the like specifically included in the terminal device are set according to actual requirements, which are not limited in this embodiment.
The processing component 40 generally controls the overall operation of the terminal device. The processing component 40 may include one or more processors 41 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 40 may include one or more modules that facilitate interactions between the processing component 40 and other components. For example, processing component 40 may include a multimedia module to facilitate interaction between multimedia component 45 and processing component 40.
The power supply assembly 44 provides power to the various components of the terminal device. Power supply components 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for terminal devices.
The multimedia component 45 comprises a display screen between the terminal device and the user providing an output interface. In some embodiments, the display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation.
The audio component 46 is configured to output and/or input audio signals. For example, the audio component 46 includes a Microphone (MIC) configured to receive external audio signals when the terminal device is in an operational mode, such as a speech recognition mode. The received audio signals may be further stored in the memory 42 or transmitted via the communication component 43. In some embodiments, audio assembly 46 further includes a speaker for outputting audio signals.
The input/output interface 47 provides an interface between the processing assembly 40 and peripheral interface modules, which may be click wheels, buttons, etc. These buttons may include, but are not limited to: volume button, start button and lock button.
The sensor assembly 48 includes one or more sensors for providing status assessment of various aspects for the terminal device. For example, the sensor assembly 48 may detect the open/closed state of the terminal device, the relative positioning of the assembly, the presence or absence of user contact with the terminal device. The sensor assembly 48 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 48 may also include a camera or the like.
The communication component 43 is configured to facilitate communication between the terminal device and other devices in a wired or wireless manner. The terminal device may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot, where the SIM card slot is used to insert a SIM card, so that the terminal device may log into a GPRS network, and establish communication with a server through the internet.
From the above, it will be appreciated that the communication component 43, the audio component 46, and the input/output interface 47, the sensor component 48 referred to in the embodiment of fig. 4 may be implemented as an input device in the embodiment of fig. 3.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A high-speed dynamic image detection method, characterized by comprising:
collecting original image data;
dividing the original image data according to a target speed parameter to obtain divided image data;
inputting the segmented image data into an identification matrix to obtain a first foreground target;
and identifying the first foreground object according to the dynamic identification model to obtain a second foreground object.
2. The method of claim 1, wherein after the acquiring of the raw image data, the method further comprises:
and carrying out minimum binarization processing on the original image data.
3. The method of claim 1, wherein segmenting the image data to be detected according to the target speed parameter to obtain segmented image data comprises:
extracting motion parameters in the original image data;
calculating a target speed parameter of the original image data according to the motion parameter;
matching the target speed parameter with a preset speed threshold matrix to obtain an image segmentation strategy;
and dividing the original image data according to the image division strategy to obtain the divided image data.
4. The method of claim 1, wherein the inputting the segmented image data into the recognition matrix to obtain the first foreground object comprises:
matching and screening the segmented image by using the identification matrix to obtain the first foreground object, wherein the first foreground object comprises at least one moving object, and the identification matrix is
Where P1 Pn are a number of segmented image data and M1 through Mn are a number of matching image data (i.e., first foreground objects).
5. A high-speed dynamic image detection apparatus, comprising:
the acquisition module is used for acquiring original image data;
the segmentation module is used for segmenting the original image data according to the target speed parameter to obtain segmented image data;
the input module is used for inputting the segmented image data into the recognition matrix to obtain a first foreground target;
and the identification module is used for identifying the first foreground object according to the dynamic identification model to obtain a second foreground object.
6. The apparatus of claim 5, wherein the apparatus further comprises:
and the processing module is used for carrying out minimum binarization processing on the original image data.
7. The apparatus of claim 5, wherein the partitioning module comprises:
an extracting unit for extracting motion parameters in the original image data;
a calculation unit for calculating a target speed parameter of the original image data according to the motion parameter;
the matching unit is used for matching the target speed parameter with a preset speed threshold matrix to obtain an image segmentation strategy;
and the segmentation unit is used for segmenting the original image data according to the image segmentation strategy to obtain the segmented image data.
8. The apparatus of claim 5, wherein the input module comprises:
the screening unit is used for matching and screening the segmented image by utilizing the identification matrix to obtain the first foreground object, wherein the first foreground object comprises at least one moving object, and the identification matrix is
Where P1 Pn are a number of segmented image data and M1 through Mn are a number of matching image data (i.e., first foreground objects).
9. A non-volatile storage medium, characterized in that the non-volatile storage medium comprises a stored program, wherein the program, when run, controls a device in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 4.
10. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for executing the processor, wherein the computer readable instructions when executed perform the method of any of claims 1 to 4.
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