CN113743222A - Body temperature measuring method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The embodiment of the disclosure discloses a body temperature measuring method, a body temperature measuring device, electronic equipment and a readable storage medium. The body temperature measuring method comprises the following steps: acquiring video data; identifying a temperature measurement area of a video frame to be measured in the video data; determining the highest temperature in the temperature measuring area; detecting a human face area in the video frame to be subjected to temperature measurement under the condition that the highest temperature of the temperature measurement area exceeds a normal body temperature range; and outputting the temperature of the face area. According to the method, the temperature measuring area is firstly identified, and the face detection algorithm is started only when the temperature measuring area exceeds the normal body temperature range, so that the occupation of system resources is greatly reduced.
Description
Technical Field
The present disclosure relates to the field of video technologies, and in particular, to a body temperature measurement method, an apparatus, an electronic device, and a readable storage medium.
Background
Influenced by epidemic situations, the uncooperative temperature measurement technology is widely concerned. Uncooperative thermometry is typically based on thermal imaging principles, such as image processing techniques, to detect body temperature. The inventor finds that the method consumes a large amount of computing power on face detection, so that the algorithm consumes a large amount of time, and the system resource occupies a large amount.
Disclosure of Invention
In order to solve the problems in the related art, embodiments of the present disclosure provide a body temperature measurement method, device, electronic device, and readable storage medium.
In a first aspect, a body temperature measurement method is provided in an embodiment of the present disclosure.
Specifically, the body temperature measuring method comprises the following steps:
acquiring video data;
identifying a temperature measurement area of a video frame to be measured in the video data;
determining the highest temperature in the temperature measuring area;
detecting a human face area in the video frame to be subjected to temperature measurement under the condition that the highest temperature of the temperature measurement area exceeds a normal body temperature range;
and outputting the temperature of the face area.
Optionally, the video data is thermal imaging video data.
With reference to the first aspect, in a first implementation manner of the first aspect, the identifying a temperature measurement area of a video frame to be measured in the video data includes:
acquiring the background temperature of a visual field area;
determining a target area in an effective body temperature range from the video frame to be tested;
and determining the area with the temperature difference larger than the temperature threshold value from the target area as a temperature measuring area.
With reference to the first aspect, in a second implementation manner of the first aspect, the identifying a temperature measurement area of a video frame to be measured in the video data includes:
acquiring background temperature and background score in a visual field area;
selecting a pixel point in the video frame to be subjected to temperature measurement as a current pixel point;
under the condition that the temperature of the current pixel point is within the effective body temperature range, determining whether the current pixel point belongs to a temperature measurement area or not according to whether the difference value between the temperature of the current pixel point and the background temperature of the pixel point is larger than a temperature threshold value or not;
adjusting the background score of the current pixel point through a preset variable quantity, and if the background score meets a score threshold condition, updating the background temperature of the current pixel point and resetting the background score;
and selecting the next pixel point until all pixel points are traversed, and determining the temperature measuring area.
Optionally, the method further comprises: initializing a background temperature before the adjusting the background score of the current pixel point by a predetermined variation.
With reference to the second implementation manner of the first aspect, in a third implementation manner of the first aspect, the identifying a temperature measurement area of a video frame to be measured in the video data further includes:
and under the condition that the temperature of the current pixel point exceeds the effective body temperature range, determining that the current pixel point does not belong to a temperature measurement area, updating the background temperature of the current pixel point and resetting the background score.
With reference to any one of the first to third implementation manners of the first aspect, in a fourth implementation manner of the first aspect, the identifying the temperature measurement area of the video frame to be measured in the video data further includes optimizing the temperature measurement area through an image erosion algorithm.
With reference to the first aspect and any one of the first to fourth implementation manners of the first aspect, in a fifth implementation manner of the first aspect, the detecting a face region in the video frame to be measured includes:
and detecting the video frame to be detected through a human face detection model to obtain a human face area. Optionally, the video frame of the temperature to be detected is detected by a thermal imaging face detection model to obtain a face region.
With reference to the first aspect and any one of the first to fifth implementation manners of the first aspect, in a sixth implementation manner of the first aspect, the present disclosure further includes:
and under the condition that the highest temperature of the temperature measurement area is in a normal body temperature range, outputting the highest temperature of the temperature measurement area as the body temperature.
In a second aspect, a body temperature measurement device is provided in embodiments of the present disclosure.
Specifically, the body temperature measuring device includes:
an acquisition module configured to acquire video data;
the identification module is configured to identify a temperature measurement area of a video frame to be measured in the video data;
a determination module configured to determine a maximum temperature within the temperature measurement area;
the detection module is configured to detect a human face area in the video frame to be subjected to temperature measurement under the condition that the highest temperature of the temperature measurement area exceeds a normal body temperature range;
an output module configured to output a temperature of the face region.
In a third aspect, the disclosed embodiments provide an electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer instructions, and wherein the one or more computer instructions are executed by the processor to implement the foregoing method.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the foregoing method.
According to the technical scheme provided by the embodiment of the disclosure, video data (optionally, the video data is thermal imaging video data) is acquired; identifying a temperature measurement area of a video frame to be measured in the video data; determining the highest temperature in the temperature measuring area; detecting a human face area in the video frame to be subjected to temperature measurement under the condition that the highest temperature of the temperature measurement area exceeds a normal body temperature range; and outputting the temperature of the face area. According to the method, the temperature measuring area is firstly identified, and the face detection algorithm is started only when the temperature measuring area exceeds the normal body temperature range, so that the occupation of system resources is greatly reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:
fig. 1 shows a flow chart of a method of body temperature measurement according to an embodiment of the present disclosure;
FIG. 2 shows a flow chart of a method of body temperature measurement according to another embodiment of the present disclosure;
FIG. 3 illustrates a flow chart for identifying a temperature measurement zone according to an embodiment of the present disclosure;
FIG. 4 shows a schematic diagram of a video frame of a temperature to be measured according to an embodiment of the present disclosure;
FIG. 5 shows a flow diagram of identifying a temperature measurement zone according to another embodiment of the present disclosure;
FIG. 6 shows a flow diagram of identifying a temperature measurement zone according to yet another embodiment of the present disclosure;
FIG. 7 shows a block diagram of a body temperature measurement device according to an embodiment of the present disclosure;
FIG. 8 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure;
fig. 9 shows a schematic structural diagram of a computer system suitable for implementing a body temperature measurement method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.
In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof may be present or added.
It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 shows a flow chart of a body temperature measurement method according to an embodiment of the present disclosure.
As shown in fig. 1, the method includes operations S110 to S150.
In operation S110, video data is acquired.
In operation S120, a temperature measurement region of a video frame to be measured in the video data is identified.
In operation S130, a highest temperature within the temperature measuring region is determined.
In operation S140, a face region in the video frame to be temperature measured is detected when the highest temperature of the temperature measurement region exceeds the normal body temperature range.
In operation S150, the temperature of the face region is output.
According to the technical scheme provided by the embodiment of the disclosure, video data is acquired; identifying a temperature measurement area of a video frame to be measured in the video data; determining the highest temperature in the temperature measuring area; detecting a human face area in the video frame to be subjected to temperature measurement under the condition that the highest temperature of the temperature measurement area exceeds a normal body temperature range; and outputting the temperature of the face area. According to the method, the temperature measuring area is firstly identified, and the face detection algorithm is started only when the temperature measuring area exceeds the normal body temperature range, so that the occupation of system resources is greatly reduced.
The body temperature measuring method disclosed by the embodiment of the disclosure can be applied to the scene of face access control, and can also be applied to various public places, such as subway stations, libraries and the like.
The video data of the disclosed embodiments may be thermal imaging video data. The thermal imaging sensor (for example, an infrared sensor) converts the detected radiation energy of the object into an electric signal in a non-contact manner, and the electric signal is processed by the system and converted into a gray-scale or pseudo-color image, namely thermal imaging. By continuously detecting, thermal imaging at multiple time instants constitutes thermal imaging video data.
According to the embodiments of the present disclosure, thermal imaging video data may be acquired by a thermal imaging sensor disposed at a fixed position. Because the thermal imaging sensor is arranged at a fixed position, most background objects cannot move, and the method disclosed by the embodiment of the invention can simplify the human body temperature measurement process through background modeling.
According to the embodiment of the disclosure, the temperature measurement area of the video frame to be measured in the video data can be identified based on the correlation between the video frame sequences. In this way, an object that remains stationary for a long period of time in the field of view can be identified as background, eliminating the need to take a temperature measurement of the object. It should be noted that in operation 120, no recognition algorithm for a human face or other specific target is used, so that a complex recognition operation is avoided, and occupation of system resources is reduced.
According to the embodiment of the disclosure, after the temperature measurement area is determined, the highest temperature in the temperature measurement area can be determined. In the thermographic image, the value of each pixel represents the temperature at that location, and therefore, the temperature measurement region can be traversed to find the highest temperature value.
According to the embodiment of the disclosure, if the highest temperature of the temperature measurement area exceeds the normal body temperature range, the human face area in the temperature measurement video frame is detected. The normal body temperature range refers to a body temperature range of a healthy normal person, and may be set to less than 37 ℃ or 37.5 ℃, for example. When the temperature exceeds the range, for example, 39 ℃, the human face area in the video frame to be tested is further detected, and the temperature measurement result is obtained again in the human face area and output to reduce the misjudgment.
For example, if a person has a body temperature of 36 ℃ and walks into a detection field with a cup held by a hand at 39 ℃, both the human body and the cup may be identified as temperature measurement regions by operation S120, and thus, the temperature of the human body may be identified as 39 ℃ in operation S130, thereby generating erroneous judgment. Through operations S140 and S150, the face region may be further determined to exclude such misjudgment.
According to the embodiment of the disclosure, the video frame to be detected can be detected through a thermal imaging face detection model so as to acquire the face region. The thermal imaging face detection model may be various artificial intelligence models, such as a convolutional neural network model, or may select other suitable non-deep learning models. The convolutional neural network model can be specially trained based on training data of thermal imaging, or is obtained by a human face detection model based on standard visual images through transfer learning.
According to the embodiment of the present disclosure, a thermal imaging face detection model may not be used, for example, other sensors may be used to obtain face position auxiliary information for determining a face region, which is not limited in the present disclosure.
According to the embodiment of the disclosure, under the condition that the highest temperature of the temperature measurement area is in the normal body temperature range, the highest temperature of the temperature measurement area is output as the body temperature. Optionally, a minimum body temperature may also be set, for example 33 ℃ or 35 ℃. If the temperature of the temperature measurement area is within the normal body temperature range, even is lower than the lowest body temperature or the temperature measurement area is not detected, the temperature measurement result can be directly output, and the possibility that normal people are identified as fever patients does not exist.
Fig. 2 shows a flow chart of a body temperature measurement method according to another embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S210 to S250.
In operation S210, a temperature measurement area of a video frame to be measured in the video data is identified by a background modeling logic temperature measurement method, and a highest temperature in the temperature measurement area is determined.
In operation S220, it is determined whether the maximum temperature is within the normal body temperature range, and if it is out of the normal body temperature range, operation S230 is performed, otherwise operation S250 is performed.
In operation S230, face detection (e.g., thermal imaging face detection) is performed to obtain a face region.
In operation S240, a temperature measurement is performed on the face region.
In operation S250, a body temperature is output.
The method disclosed by the embodiment of the invention firstly identifies the temperature of the temperature measurement area, directly outputs the body temperature when the temperature measurement is in accordance with the temperature range of the human body, and otherwise finds out the face area through face detection to measure the temperature, thereby greatly reducing the occupation of system resources.
Referring back to fig. 1, operation S120 may be implemented in various ways according to an embodiment of the present disclosure. The following description is made with reference to various embodiments illustrated in fig. 3 to 6, which describe a temperature measurement region determination method based on temperature background modeling, but the disclosure is not limited thereto, and those skilled in the art may also use other temperature background establishment methods, or use other global temperature measurement methods instead of the temperature background modeling method.
Fig. 3 shows a flow chart for identifying a temperature measurement zone according to an embodiment of the present disclosure.
As shown in fig. 3, the method includes operations S310 to S330.
In operation S310, a background temperature of a field of view region is acquired.
In operation S320, a target region in an effective body temperature range is determined from the video frame of the temperature to be measured.
In operation S330, a temperature measurement region is determined from the target region, in which a temperature difference from the background temperature is greater than a temperature threshold.
In accordance with an embodiment of the present disclosure, a thermal imaging sensor is disposed in a fixed position having a fixed field of view, each position within the field of view including an attribute parameter indicative of a background temperature of the position. The position may mean that each pixel point is a position, or a region composed of a plurality of pixel points is a position.
According to an embodiment of the present disclosure, the effective body temperature range refers to a possible temperature range of the human body, and the temperature beyond the effective body temperature range is not considered as the body temperature of the human body. For example, the lower limit of the effective body temperature range may be set to 33 ℃ or 35 ℃ and the upper limit may be set to 40 ℃ or 42 ℃.
According to the embodiment of the disclosure, the region in the effective body temperature range in the video frame to be measured can be determined as the target region, and then the temperature of the target region is compared with the background temperature to select the temperature measurement region. For example, there is an object with a fixed temperature of 37 ℃ in the field of view, in the technical solution of the embodiment of the present disclosure, because of the existence of the object, the background temperature of the position where the object is located is set to be 37 ℃, in the subsequent temperature detection, because there is no large temperature difference, the area is not identified as the temperature measurement area, and the interference of the object to the body temperature measurement is avoided.
On the contrary, if the background temperature of a certain position is 20 ℃, when a person enters the area, the temperature difference between the body temperature of the person and the background temperature of 20 ℃ is large, and the area can be judged as a temperature measurement area through a threshold value. The threshold may be set according to actual conditions, and may be set to 10 ℃. The threshold is used for measuring the difference between the current temperature and the background temperature, and the absolute value of the temperature difference can be directly taken to be compared with the threshold.
Fig. 4 shows a schematic diagram of a video frame of a temperature to be measured according to an embodiment of the present disclosure.
As shown in FIG. 4, the background temperature in region A is about 36 deg.C and the background temperature in other regions is between 15 deg.C and 20 deg.C. In a video frame to be measured at a certain moment, the temperatures of the area A and the area B are both detected to be 36 ℃, and the temperature of the other area C is between 15 ℃ and 20 ℃. In determining the temperature measurement region, the region a and the region B in the effective body temperature region are first determined, and then the region a and the region B are compared with the background temperature. And the temperature difference between the area A and the background temperature is small, the threshold condition is not met, and the area A is determined to be a non-temperature measuring area. And the temperature difference between the area B and the background temperature is larger, so that the area B meets the threshold condition and is determined as a temperature measurement area. And finally determining the area B in the video frame to be measured as a temperature measurement area.
An alternative implementation of the background modeling of the present disclosure is described below in conjunction with fig. 5 and 6. Background modeling, i.e., a process of determining the background temperature within the field of view and updating in real time.
Fig. 5 shows a flow chart for identifying a temperature measurement zone according to another embodiment of the present disclosure.
As shown in fig. 5, the method includes operations S510 to S550.
In operation S510, a background temperature and a background score within a field of view region are acquired.
In operation S520, a pixel point in the video frame to be measured is selected as a current pixel point.
In operation S530, under the condition that the temperature of the current pixel is within the effective body temperature range, it is determined whether the current pixel belongs to the temperature measurement region according to whether a difference between the temperature of the current pixel and the background temperature of the pixel is greater than a temperature threshold.
In operation S540, the background score of the current pixel point is adjusted by a predetermined variation, and if the background score satisfies a score threshold condition, the background temperature of the current pixel point is updated and the background score is reset.
In operation S550, a next pixel is selected until all the pixels are traversed, and a temperature measurement area is determined.
According to embodiments of the present disclosure, the background score is used to identify stationary items within the range of effective body temperature to decide how to update the background temperature. The background temperature and background score may be initially assigned by initialization and then continuously iterated through the detection process.
According to the embodiment of the present disclosure, traversal may be performed in units of pixel points, and similarly to S320 and S330 above, the temperature measurement region is determined by the effective body temperature range and the temperature difference value.
According to the embodiment of the disclosure, in the traversal process, if the temperature of the pixel point is within the effective body temperature range, the background score of the current pixel point is adjusted through the preset variable quantity after whether the pixel point is in the temperature measuring region is determined. The adjustment of the predetermined variation has a cumulative effect on the background score over time during the continuous detection process.
For example, the background score may be initialized to a full value, e.g., 255, and then the value of the background score for the region where the temperature has not changed significantly may be attenuated by decreasing each round by a predetermined amount of change. When the attenuation reaches a certain threshold value, namely the temperature is kept for a period of time and is not changed, the position of the pixel point is not considered to be the temperature measurement concerned position, the pixel point is determined to be a background area, and the temperature of the position can be used for updating the background temperature. In the subsequent temperature measurement process, if the temperature change does not occur, the temperature change is not identified as a temperature measurement area because the temperature difference condition is not met any more, so that the interference of the area on the temperature measurement result is avoided. Optionally, the background score may also be reset after updating the background temperature using the temperature of the location.
Obviously, as an equivalent implementation, it is also possible to initialize the background score to a smaller value, increase it by a predetermined variation amount for each round, and update the background temperature at the location when the background score exceeds a certain threshold.
According to an embodiment of the present disclosure, the identifying a temperature measurement area of a video frame to be measured in the video data further includes:
and under the condition that the temperature of the current pixel point exceeds the effective body temperature range, determining that the current pixel point does not belong to a temperature measurement area, updating the background temperature of the current pixel point and resetting the background score.
For example, if the temperature of a certain pixel point is 25 ℃, the subsequent temperature difference judgment cannot be performed, the pixel point is directly determined not to belong to the target area, and not to belong to the temperature measurement area, the background temperature of the position can be updated by using the temperature of the pixel point, and the background score is reset.
The above method is described below with reference to the specific embodiment shown in fig. 6.
Fig. 6 shows a flow chart for identifying a temperature measurement zone according to yet another embodiment of the present disclosure.
As shown in fig. 6, the method includes operations S601 to S611.
In operation S601, a pixel point is selected from the video frame to be measured, and it is determined whether the pixel point is within the effective body temperature range, if so, operation S602 is continuously performed, otherwise, operation S611 is performed.
In operation S602, an absolute value of a difference between the temperature of the current pixel and the background temperature of the pixel is calculated.
In operation S603, it is determined whether the absolute value of the difference is greater than a preset temperature threshold, and if so, operation S604 is performed, otherwise, operation S608 is performed.
In operation S604, the pixel point is marked as a temperature measurement area.
In operation S605, it is determined whether the background has not been initialized, if not, operation S606 is performed, otherwise, operation S607 is performed.
In operation S606, a background temperature is initialized.
Here, the initialization process of S605 and S606 may also be set before the whole process, and this is not limited by the embodiment of the present disclosure.
In operation S607, the background score is self-decremented and the background is updated if the score is too low.
In operation S608, the pixel point is marked as a non-temperature measurement region, and it jumps to operation S607.
In operation S609, it is determined whether the background processing of all the pixels is completed, if so, operation S610 is performed, otherwise, operation S601 is returned to, and a next pixel is selected.
In operation S610, the temperature measurement region is traversed to obtain the highest temperature.
In operation S611, the pixel point is marked as a non-thermometric area, and the process jumps to operation S609.
According to the method, the temperature measuring area is identified by using the logic temperature measuring method, the calculated amount is small, the occupied system resources are small, and the body temperature under most scenes can be identified rapidly and correctly. For the body temperature result of the suspected febrile patient, the operations S140 and 150 (or S230-S250) described above are supplemented, so as to minimize the probability of erroneous judgment.
According to the embodiment of the disclosure, the identifying the temperature measurement area of the video frame to be measured in the video data further comprises optimizing the temperature measurement area through an image corrosion algorithm. This operation may be set between the above operations S609 and S610.
The erosion algorithm is an algorithm that eliminates boundary points, shrinking the boundaries inward, and can be used to eliminate small and meaningless objects. For example, a convolution kernel of 3 × 3 may be used to scan each pixel of the image, and "the structuring element with the binary image it overlays, resulting in a pixel of the image that is 1 if both are 1, and 0 otherwise. By the above operation, the boundary can be narrowed. According to the embodiment of the disclosure, the temperature measurement area is optimized through an image corrosion algorithm, so that the influence caused by the edge temperature can be avoided.
In summary, the technical scheme of the embodiment of the disclosure combines the empirical logic and the deep learning algorithm, so that the temperature measurement accuracy is ensured, the calculated amount is remarkably reduced, the occupation of system resources is reduced, and the temperature measurement efficiency is improved.
Fig. 7 shows a block diagram of a body temperature measurement device 700 according to an embodiment of the present disclosure. The apparatus 700 may be implemented as part or all of an electronic device through software, hardware, or a combination of both.
As shown in fig. 7, the body temperature measurement device 700 includes an acquisition module 710, an identification module 720, a determination module 730, a detection module 740, and an output module 750.
An acquisition module 710 configured to acquire video data;
an identifying module 720, configured to identify a temperature measurement region of a video frame to be measured in the video data;
a determination module 730 configured to determine a maximum temperature within the temperature measurement area;
the detection module 740 is configured to detect a face region in the video frame to be subjected to temperature measurement when the highest temperature of the temperature measurement region exceeds a normal body temperature range;
an output module 750 configured to output a temperature of the face region.
The aforementioned video data may be thermal imaging video data.
According to the technical scheme provided by the embodiment of the disclosure, the face detection algorithm is started only when the temperature measurement area exceeds the normal body temperature range by firstly identifying the temperature measurement area, so that the occupation of system resources is greatly reduced
According to an embodiment of the present disclosure, the identifying module 720 is configured to:
acquiring the background temperature of a visual field area;
determining a target area in an effective body temperature range from the video frame to be tested;
and determining the area with the temperature difference larger than the temperature threshold value from the target area as a temperature measuring area.
According to an embodiment of the present disclosure, the identifying module 720 is configured to:
acquiring background temperature and background score in a visual field area;
selecting a pixel point in the video frame to be subjected to temperature measurement as a current pixel point;
under the condition that the temperature of the current pixel point is within the effective body temperature range, determining whether the current pixel point belongs to a temperature measurement area or not according to whether the difference value between the temperature of the current pixel point and the background temperature of the pixel point is larger than a temperature threshold value or not;
adjusting the background score of the current pixel point through a preset variable quantity, and if the background score meets a score threshold condition, updating the background temperature of the current pixel point and resetting the background score;
and selecting the next pixel point until all pixel points are traversed, and determining the temperature measuring area.
According to an embodiment of the present disclosure, the identifying module 720 is further configured to:
and under the condition that the temperature of the current pixel point exceeds the effective body temperature range, determining that the current pixel point does not belong to a temperature measurement area, updating the background temperature of the current pixel point and resetting the background score.
According to an embodiment of the present disclosure, the identification module 720 is further configured to optimize the temperature measurement region by an image erosion algorithm.
According to the embodiment of the present disclosure, the detecting the face region in the video frame to be subjected to temperature measurement includes:
and detecting the video frame to be detected through a human face detection model to obtain a human face area.
According to the embodiment of the present disclosure, the output module 750 is further configured to output the maximum temperature of the temperature measurement region as the body temperature if the maximum temperature of the temperature measurement region is in the normal body temperature range.
The present disclosure also discloses an electronic device, and fig. 8 shows a block diagram of the electronic device according to an embodiment of the present disclosure.
As shown in fig. 8, the electronic device 800 includes a memory 801 and a processor 802, wherein the memory 801 is used for storing a program that supports the electronic device to execute the body temperature measurement method or the code generation method in any of the above embodiments, and the processor 802 is configured to execute the program stored in the memory 801.
The memory 801 is configured to store one or more computer instructions that are executed by the processor 802 to implement a body temperature measurement method as described in any of the embodiments above, in accordance with embodiments of the present disclosure.
Fig. 9 shows a schematic structural diagram of a computer system suitable for implementing a body temperature measurement method according to an embodiment of the present disclosure.
As shown in fig. 9, the computer system 900 includes a processing unit 901 which can execute various processes in the above-described embodiments according to a program stored in a Read Only Memory (ROM)902 or a program loaded from a storage section 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data necessary for the operation of the system 900 are also stored. The processing unit 901, the ROM902, and the RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
The following components are connected to the I/O interface 905: an input portion 906 including a keyboard, a mouse, and the like; an output section 907 including components such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as necessary. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 910 as necessary, so that a computer program read out therefrom is mounted into the storage section 908 as necessary. The processing unit 901 may be implemented as a CPU, a GPU, a TPU, an FPGA, an NPU, or other processing units.
In particular, the above described methods may be implemented as computer software programs according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a medium readable thereby, the computer program comprising program code for performing the above-described method. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present disclosure may be implemented by software or by programmable hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.
As another aspect, the present disclosure also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the electronic device or the computer system in the above embodiments; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (10)
1. A method of body temperature measurement, comprising:
acquiring video data;
identifying a temperature measurement area of a video frame to be measured in the video data;
determining the highest temperature in the temperature measuring area;
detecting a human face area in the video frame to be subjected to temperature measurement under the condition that the highest temperature of the temperature measurement area exceeds a normal body temperature range;
and outputting the temperature of the face area.
2. The method of claim 1, wherein the identifying a temperature measurement region of a temperature measurement video frame in the video data comprises:
acquiring the background temperature of a visual field area;
determining a target area in an effective body temperature range from the video frame to be tested;
and determining the area with the temperature difference larger than the temperature threshold value from the target area as a temperature measuring area.
3. The method of claim 1, wherein the identifying a temperature measurement region of a temperature measurement video frame in the video data comprises:
acquiring background temperature and background score in a visual field area;
selecting a pixel point in the video frame to be subjected to temperature measurement as a current pixel point;
under the condition that the temperature of the current pixel point is within the effective body temperature range, determining whether the current pixel point belongs to a temperature measurement area or not according to whether the difference value between the temperature of the current pixel point and the background temperature of the pixel point is larger than a temperature threshold value or not;
adjusting the background score of the current pixel point through a preset variable quantity, and if the background score meets a score threshold condition, updating the background temperature of the current pixel point and resetting the background score;
and selecting the next pixel point until all pixel points are traversed, and determining the temperature measuring area.
4. The method of claim 3, wherein the identifying a temperature measurement region of a temperature measurement video frame in the video data further comprises:
and under the condition that the temperature of the current pixel point exceeds the effective body temperature range, determining that the current pixel point does not belong to a temperature measurement area, updating the background temperature of the current pixel point and resetting the background score.
5. A method according to any of claims 2 to 4, wherein the identifying the temperature measurement regions of the video frames of temperature to be measured in the video data further comprises optimizing the temperature measurement regions by an image erosion algorithm.
6. The method according to any one of claims 1 to 4, wherein the video data is thermal imaging video data, and the detecting the face region in the video frame to be tested comprises:
and detecting the video frame to be measured through a thermal imaging face detection model to obtain a face region.
7. The method of any of claims 1-4, further comprising:
and under the condition that the highest temperature of the temperature measurement area is in a normal body temperature range, outputting the highest temperature of the temperature measurement area as the body temperature.
8. A body temperature measurement device comprising:
an acquisition module configured to acquire video data;
the identification module is configured to identify a temperature measurement area of a video frame to be measured in the video data;
a determination module configured to determine a maximum temperature within the temperature measurement area;
the detection module is configured to detect a human face area in the video frame to be subjected to temperature measurement under the condition that the highest temperature of the temperature measurement area exceeds a normal body temperature range;
an output module configured to output a temperature of the face region.
9. An electronic device comprising a memory and a processor; wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the steps of the method of any one of claims 1 to 7.
10. A readable storage medium having stored thereon computer instructions, which when executed by a processor, perform the steps of the method of any one of claims 1 to 7.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4574359A (en) * | 1982-12-21 | 1986-03-04 | Terumo Kabushiki Kaisha | Electronic clinical thermometer, and method of measuring body temperature |
| US5710722A (en) * | 1996-05-24 | 1998-01-20 | The United States Of America As Represented By The Secretary Of The Army | Automated minimum resolvable temperature difference test for imaging infrared systems |
| US20110118608A1 (en) * | 2009-09-04 | 2011-05-19 | Bjoern Lindner | System and method for detection of a temperature on a surface of a body |
| CN103415788A (en) * | 2011-03-17 | 2013-11-27 | 松下电器产业株式会社 | Object detection device |
| CN104279688A (en) * | 2013-07-10 | 2015-01-14 | 海尔集团公司 | Human body detection method, background temperature determining method, device, and air conditioning equipment |
| US20180014790A1 (en) * | 2015-02-26 | 2018-01-18 | Koninklijke Philips N.V. | Context detection for medical monitoring |
| WO2018072387A1 (en) * | 2016-10-20 | 2018-04-26 | 中兴通讯股份有限公司 | Earphone, terminal having function of ear thermometer, and method for measuring ear temperature |
| CN108236456A (en) * | 2016-12-27 | 2018-07-03 | 北京小米移动软件有限公司 | Detect method, apparatus and the electronic thermometer that clinical thermometer is placed |
| US20200064006A1 (en) * | 2018-08-24 | 2020-02-27 | Pixart Imaging Inc. | Temperature detecting device and air conditioner |
| CN111458039A (en) * | 2020-04-14 | 2020-07-28 | 芋头科技（杭州）有限公司 | Augmented reality body temperature measurement method and device based on infrared temperature measurement camera |
| CN111507200A (en) * | 2020-03-26 | 2020-08-07 | 北京迈格威科技有限公司 | Body temperature detection method, body temperature detection device and dual-optical camera |
| CN112102207A (en) * | 2020-10-29 | 2020-12-18 | 北京澎思科技有限公司 | Method and device for determining temperature, electronic equipment and readable storage medium |
| CN112107301A (en) * | 2020-09-29 | 2020-12-22 | 深圳市优必选科技股份有限公司 | Human body temperature detection model realization method and device and human body temperature detection method |
| CN113155292A (en) * | 2021-03-30 | 2021-07-23 | 新疆爱华盈通信息技术有限公司 | Face temperature measurement method, face temperature measurement instrument and storage medium |
-
2021
- 2021-08-04 CN CN202110891255.1A patent/CN113743222A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4574359A (en) * | 1982-12-21 | 1986-03-04 | Terumo Kabushiki Kaisha | Electronic clinical thermometer, and method of measuring body temperature |
| US5710722A (en) * | 1996-05-24 | 1998-01-20 | The United States Of America As Represented By The Secretary Of The Army | Automated minimum resolvable temperature difference test for imaging infrared systems |
| US20110118608A1 (en) * | 2009-09-04 | 2011-05-19 | Bjoern Lindner | System and method for detection of a temperature on a surface of a body |
| CN103415788A (en) * | 2011-03-17 | 2013-11-27 | 松下电器产业株式会社 | Object detection device |
| CN104279688A (en) * | 2013-07-10 | 2015-01-14 | 海尔集团公司 | Human body detection method, background temperature determining method, device, and air conditioning equipment |
| US20180014790A1 (en) * | 2015-02-26 | 2018-01-18 | Koninklijke Philips N.V. | Context detection for medical monitoring |
| WO2018072387A1 (en) * | 2016-10-20 | 2018-04-26 | 中兴通讯股份有限公司 | Earphone, terminal having function of ear thermometer, and method for measuring ear temperature |
| CN108236456A (en) * | 2016-12-27 | 2018-07-03 | 北京小米移动软件有限公司 | Detect method, apparatus and the electronic thermometer that clinical thermometer is placed |
| US20200064006A1 (en) * | 2018-08-24 | 2020-02-27 | Pixart Imaging Inc. | Temperature detecting device and air conditioner |
| CN111507200A (en) * | 2020-03-26 | 2020-08-07 | 北京迈格威科技有限公司 | Body temperature detection method, body temperature detection device and dual-optical camera |
| CN111458039A (en) * | 2020-04-14 | 2020-07-28 | 芋头科技（杭州）有限公司 | Augmented reality body temperature measurement method and device based on infrared temperature measurement camera |
| CN112107301A (en) * | 2020-09-29 | 2020-12-22 | 深圳市优必选科技股份有限公司 | Human body temperature detection model realization method and device and human body temperature detection method |
| CN112102207A (en) * | 2020-10-29 | 2020-12-18 | 北京澎思科技有限公司 | Method and device for determining temperature, electronic equipment and readable storage medium |
| CN113155292A (en) * | 2021-03-30 | 2021-07-23 | 新疆爱华盈通信息技术有限公司 | Face temperature measurement method, face temperature measurement instrument and storage medium |
Non-Patent Citations (8)
| Title |
|---|
| ALI GHAHRAMANI等: "Infrared thermography of human face for monitoring thermoregulation performance and estimating personal thermal comfort", 《BUILDING AND ENVIRONMENT》, vol. 109, 9 September 2016 (2016-09-09), pages 1 - 11, XP029760285, DOI: 10.1016/j.buildenv.2016.09.005 * |
| BERG W等: "Evaluation of Skin Temperature Measurements as Suitable Surrogates of Body Temperature in Lizards under Field Conditions", 《HERPETOLOGICAL REVIEW》, vol. 46, no. 2, 31 December 2015 (2015-12-31), pages 157 * |
| NAZEER SHAHID等: "Real-time implementation of human detection in thermal imagery based on CNN", 《JOURNAL OF KIIT》, vol. 17, no. 1, 31 December 2019 (2019-12-31), pages 107 - 121 * |
| SE WOON CHO等: "Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network", 《SENSORS》, vol. 18, no. 9, 7 September 2018 (2018-09-07), pages 1 - 31 * |
| 刘爱旭: "基于CCD图像传感高精度温度测量技术研究", 《中国优秀硕士学位论文全文数据库：工程科技Ⅰ辑》, no. 2019, 15 January 2019 (2019-01-15), pages 023 - 131 * |
| 唐传佳等: "一体化红外测温监测技术研究与应用", 《安徽电气工程职业技术学院学报》, vol. 22, no. 1, 15 March 2017 (2017-03-15), pages 118 - 121 * |
| 张洁茹等: "双光谱智能体温检测与健康大数据管理系统", 《北京航空航天大学学报》, vol. 46, no. 9, 6 May 2020 (2020-05-06), pages 1739 - 1746 * |
| 贾彦翔: "密闭空间人工环境气氛控制及反应动力学研究", 《中国博士学位论文全文数据库：工程科技Ⅱ辑》, no. 2015, 15 June 2015 (2015-06-15), pages 038 - 9 * |
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