CN114140712A - Automatic image recognition and distribution system and method - Google Patents
Automatic image recognition and distribution system and method Download PDFInfo
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Abstract
The invention provides an automatic image recognition and distribution system and method, and relates to the technical field of image processing. In the invention, after a to-be-processed monitoring video sent by a video monitoring terminal device is obtained, each monitoring video frame in a plurality of monitoring video frames included in the to-be-processed monitoring video is identified to obtain a video frame identification result corresponding to each monitoring video frame, wherein the video frame identification result is used for representing the probability of violation information in the corresponding monitoring video frame; determining video frame correlation relation information among the multiple monitoring video frames based on a video frame identification result of each monitoring video frame in the multiple monitoring video frames; and respectively sending the multi-frame monitoring video frames to corresponding image storage equipment based on the video frame correlation information among the multi-frame monitoring video frames. Based on the method, the problem of poor control effect on the monitoring video frame in the prior art can be solved.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an automatic image recognition and distribution system and method.
Background
Video monitoring is an important monitoring means, is widely applied to various industries and is an important guarantee for safety in various fields such as production, life and the like. Among them, in video surveillance, the image recognition technology is an irreplaceable technology. In the prior art, after image recognition is performed on a video frame in a surveillance video, the surveillance video is generally sent to a corresponding device for storage, such as tracing, but in the prior art, the surveillance video is generally sent to a device for storage as a whole, and thus, the problem of poor management and control effect may be caused.
Disclosure of Invention
In view of the above, the present invention provides an automatic image recognition and distribution system and method to solve the problem of poor control effect on surveillance video frames in the prior art.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
an automatic image recognition and distribution method is applied to a video monitoring server, the video monitoring server is in communication connection with a video monitoring terminal device, and the automatic image recognition and distribution method comprises the following steps:
after a to-be-processed monitoring video sent by the video monitoring terminal equipment is obtained, identifying each monitoring video frame in a plurality of monitoring video frames included in the to-be-processed monitoring video to obtain a video frame identification result corresponding to each monitoring video frame, wherein the to-be-processed monitoring video includes the plurality of monitoring video frames, the plurality of monitoring video frames are obtained by carrying out image acquisition on a target monitoring area based on the video monitoring terminal equipment, and the video frame identification result is used for representing the probability of illegal behavior information existing in the corresponding monitoring video frame;
determining video frame correlation relation information among the multiple monitoring video frames based on a video frame identification result of each monitoring video frame in the multiple monitoring video frames;
and respectively sending the multiple frames of monitoring video frames to corresponding image storage equipment based on the video frame correlation information among the multiple frames of monitoring video frames, wherein the image storage equipment is used for storing the received monitoring video frames.
In some preferred embodiments, in the automatic image recognition and distribution method, the step of determining video frame correlation relationship information between the multiple frames of surveillance video frames based on a video frame recognition result of each of the multiple frames of surveillance video frames includes:
classifying the multiple monitoring video frames based on a video frame identification result of each monitoring video frame in the multiple monitoring video frames to obtain at least one corresponding video frame classification set, wherein each video frame classification set comprises at least one monitoring video frame;
and determining video frame correlation relation information among the multiple monitoring video frames based on the at least one video frame classification set.
In some preferred embodiments, in the automatic image recognition and distribution method, the step of classifying the multiple frames of surveillance video frames based on a video frame recognition result of each of the multiple frames of surveillance video frames to obtain at least one corresponding video frame classification set includes:
determining a probability interval to which the probability represented by the video frame identification result corresponding to each monitoring video frame in the multi-frame monitoring video frames belongs in a plurality of pre-configured probability intervals aiming at each monitoring video frame in the multi-frame monitoring video frames to obtain a target probability interval to which the monitoring video frame belongs;
and aiming at each target probability interval, constructing a corresponding video frame classification set based on each monitoring video frame corresponding to the target probability interval.
In some preferred embodiments, in the automatic image recognition and distribution method, the step of classifying the multiple frames of surveillance video frames based on a video frame recognition result of each of the multiple frames of surveillance video frames to obtain at least one corresponding video frame classification set includes:
clustering the multiple frames of monitoring video frames based on the probability represented by the video frame identification result of each frame of monitoring video frames in the multiple frames of monitoring video frames to obtain at least one video frame cluster corresponding to the multiple frames of monitoring video frames, wherein each video frame cluster in the at least one video frame cluster comprises at least one frame of monitoring video frame;
and aiming at each video frame cluster in the at least one video frame cluster, constructing a corresponding video frame classification set based on each monitoring video frame included in the video frame cluster.
In some preferred embodiments, in the above automatic image recognition distribution method, the step of determining video frame correlation relationship information between the plurality of monitoring video frames based on the at least one video frame classification set includes:
for each two frames of monitoring video frames in the multi-frame monitoring video frames, calculating the difference value between the probabilities represented by the video frame identification results corresponding to the two frames of monitoring video frames to obtain the probability difference value corresponding to the two frames of monitoring video frames, and calculating the sum value of the probabilities represented by the video frame identification results corresponding to the two frames of monitoring video frames to obtain the probability sum value corresponding to the two frames of monitoring video frames;
calculating the ratio of the probability difference value corresponding to the two frames of the surveillance video frames to the probability sum value corresponding to the two frames of the surveillance video frames aiming at each two frames of the multiple frames of the surveillance video frames to obtain the probability similarity corresponding to the two frames of the surveillance video frames;
determining whether the two surveillance video frames belong to the same video frame classification set in the at least one video frame classification set or not aiming at every two surveillance video frames in the multi-frame surveillance video frames;
for each two frames of monitoring video frames in the multi-frame monitoring video frames, if the two frames of monitoring video frames belong to the same video frame classification set in the at least one video frame classification set, determining the probability similarity corresponding to the two frames of monitoring video frames as video frame correlation relation information between the two frames of monitoring video frames;
for each two frames of monitoring video frames in the multiple frames of monitoring video frames, if the two frames of monitoring video frames belong to two different video frame classification sets in the at least one video frame classification set, calculating an average value of probability similarities corresponding to each two frames of monitoring video frames between the two video frame classification sets to obtain a similarity mean value corresponding to the two video frame classification sets, and fusing based on the probability similarities corresponding to the two frames of monitoring video frames and the similarity mean values corresponding to the two video frame classification sets to obtain video frame correlation information between the two frames of monitoring video frames.
In some preferred embodiments, in the above automatic image recognition and distribution method, the step of calculating, for each two frames of surveillance video frames in the multiple frames of surveillance video frames, an average value of probability similarities corresponding to each two frames of surveillance video frames between the two video frame classification sets if the two frames of surveillance video frames belong to two different video frame classification sets in the at least one video frame classification set, to obtain a similarity average value corresponding to the two video frame classification sets, and obtaining video frame correlation information between the two frames of surveillance video frames based on the fusion of the probability similarities corresponding to the two frames of surveillance video frames and the similarity average values corresponding to the two video frame classification sets includes:
for each two frames of monitoring video frames in the multiple frames of monitoring video frames, if the two frames of monitoring video frames belong to two different video frame classification sets in the at least one video frame classification set, calculating an average value of probability similarities corresponding to each two frames of monitoring video frames between the two video frame classification sets to obtain a similarity average value corresponding to the two video frame classification sets;
and aiming at every two monitoring video frames in the multi-frame monitoring video frames, calculating the product between the probability similarity corresponding to the two monitoring video frames and the similarity mean value corresponding to the two video frame classification sets to obtain the video frame correlation relation information between the two monitoring video frames.
In some preferred embodiments, in the automatic image recognition and distribution method, the step of respectively sending the multiple frames of surveillance video frames to corresponding image storage devices based on the video frame correlation information among the multiple frames of surveillance video frames includes:
based on the video frame correlation relation information among the multiple monitoring video frames, clustering the multiple monitoring video frames to obtain at least one video frame set corresponding to the multiple monitoring video frames, wherein each video frame set comprises at least one monitoring video frame;
and aiming at each video frame set in the at least one video frame set, sending each monitoring video frame included in the video frame set to the same image storage device for storage.
The embodiment of the invention also provides an automatic image recognition and distribution system, which is applied to a video monitoring server, wherein the video monitoring server is in communication connection with a video monitoring terminal device, and the automatic image recognition and distribution system comprises:
the monitoring video frame identification module is used for identifying each monitoring video frame in a plurality of monitoring video frames included in the monitoring video to be processed after the monitoring video to be processed sent by the video monitoring terminal equipment is obtained, and obtaining a video frame identification result corresponding to each monitoring video frame, wherein the monitoring video to be processed comprises a plurality of monitoring video frames, the plurality of monitoring video frames are obtained by carrying out image acquisition on a target monitoring area based on the video monitoring terminal equipment, and the video frame identification result is used for representing the probability of illegal behavior information existing in the corresponding monitoring video frame;
the video frame correlation determination module is used for determining video frame correlation information among the multiple monitoring video frames based on the video frame identification result of each monitoring video frame in the multiple monitoring video frames;
and the monitoring video frame distribution module is used for respectively sending the multiple monitoring video frames to corresponding image storage equipment based on the video frame correlation information among the multiple monitoring video frames, wherein the image storage equipment is used for storing the received monitoring video frames.
In some preferred embodiments, in the automatic image recognition and distribution system, the video frame correlation determination module is specifically configured to:
classifying the multiple monitoring video frames based on a video frame identification result of each monitoring video frame in the multiple monitoring video frames to obtain at least one corresponding video frame classification set, wherein each video frame classification set comprises at least one monitoring video frame;
and determining video frame correlation relation information among the multiple monitoring video frames based on the at least one video frame classification set.
In some preferred embodiments, in the automatic image recognition and distribution system, the surveillance video frame distribution module is specifically configured to:
based on the video frame correlation relation information among the multiple monitoring video frames, clustering the multiple monitoring video frames to obtain at least one video frame set corresponding to the multiple monitoring video frames, wherein each video frame set comprises at least one monitoring video frame;
and aiming at each video frame set in the at least one video frame set, sending each monitoring video frame included in the video frame set to the same image storage device for storage.
After each of the plurality of frames of surveillance video frames included in the obtained surveillance video to be processed is identified and processed to obtain the video frame identification result corresponding to each frame of surveillance video frame, the video frame correlation information between the plurality of frames of surveillance video frames can be determined based on the video frame identification result of each frame of surveillance video frame, so that the plurality of frames of surveillance video frames can be respectively sent to the corresponding image storage devices based on the video frame correlation information between the plurality of frames of surveillance video frames, that is, the reliability and rationality of the image storage devices in controlling and controlling the distribution of the surveillance video frames are realized, and the problem of poor control effect of the surveillance video frames in the prior art is solved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of a video monitoring server according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of an automatic image recognition and distribution method according to an embodiment of the present invention.
Fig. 3 is a schematic block diagram of an automatic image recognition and distribution system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a video monitoring server. Wherein the video surveillance server may include a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The memory can have stored therein at least one software function (computer program) which can be present in the form of software or firmware. The processor may be configured to execute the executable computer program stored in the memory, so as to implement the automatic image recognition and distribution method provided by the embodiment of the present invention, as described later.
It is understood that in an alternative implementation, the Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Read Only Memory (EPROM), an electrically Erasable Read Only Memory (EEPROM), and the like.
It will be appreciated that in an alternative implementation, the Processor may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), and the like. In another alternative implementation, the processor may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components.
It will be appreciated that in an alternative implementation, the structure shown in fig. 1 is merely illustrative, and the video surveillance server may further include more or fewer components than those shown in fig. 1, or have a different configuration than that shown in fig. 1, for example, may include a communication unit for information interaction with other devices (e.g., video surveillance terminal devices, etc.).
The embodiment of the invention also provides an automatic image recognition and distribution method (as shown in fig. 2), which can be applied to the video monitoring server. The method steps defined by the flow related to the automatic image recognition and distribution method can be realized by the video monitoring server, and the video monitoring server is in communication connection with video monitoring terminal equipment.
The specific process shown in FIG. 2 will be described in detail below.
Step S100, after the to-be-processed monitoring video sent by the video monitoring terminal device is obtained, identifying each monitoring video frame in a plurality of monitoring video frames included in the to-be-processed monitoring video, and obtaining a video frame identification result corresponding to each monitoring video frame.
In this embodiment of the present invention, when step S100 is executed, the video monitoring server may perform, after acquiring the to-be-processed monitoring video sent by the video monitoring terminal device, identification processing on each monitoring video frame in multiple monitoring video frames included in the to-be-processed monitoring video, so as to obtain a video frame identification result corresponding to each monitoring video frame. The monitoring video to be processed comprises a plurality of monitoring video frames, the plurality of monitoring video frames are obtained by carrying out image acquisition on a target monitoring area based on the video monitoring terminal equipment, and the video frame identification result is used for representing the probability of violation information existing in the corresponding monitoring video frame.
Step S200, determining video frame correlation relation information among the multiple monitoring video frames based on the video frame identification result of each monitoring video frame in the multiple monitoring video frames.
In this embodiment of the present invention, when performing step S200, the video monitoring server may determine video frame correlation information between the multiple frames of monitoring video frames based on a video frame identification result of each of the multiple frames of monitoring video frames.
Step S300, respectively sending the multiple monitoring video frames to corresponding image storage devices based on the video frame correlation information among the multiple monitoring video frames.
In this embodiment of the present invention, when performing step S300, the video monitoring server may send the multiple frames of monitoring video frames to corresponding image storage devices respectively based on the video frame correlation information among the multiple frames of monitoring video frames. The image storage device is used for storing the received monitoring video frame.
Based on the automatic image identification and distribution method, after each monitoring video frame in the multiple monitoring video frames included in the monitoring video to be processed is identified to obtain the video frame identification result corresponding to each monitoring video frame, the video frame correlation relationship information among the multiple monitoring video frames can be determined based on the video frame identification result of each monitoring video frame, so that the multiple monitoring video frames can be respectively sent to the corresponding image storage equipment based on the video frame correlation relationship information among the multiple monitoring video frames, that is, the reliability and rationality of the image storage equipment for distribution control among the monitoring video frames are realized, and the problem of poor control effect of the monitoring video frames in the prior art is solved.
It is to be understood that, in an alternative implementation, the step S100 in the above example may further include the following steps S110, S120 and S130.
Step S110, acquiring the to-be-processed monitoring video sent by the video monitoring terminal equipment.
In this embodiment of the present invention, when step S110 is executed, the video monitoring server may obtain a to-be-processed monitoring video sent by the video monitoring terminal device. The to-be-processed monitoring video comprises a plurality of monitoring video frames, and the plurality of monitoring video frames are obtained by carrying out image acquisition on a target monitoring area based on the video monitoring terminal equipment.
Step S120, determining a video frame association degree between two frames of the monitoring video frames included in the to-be-processed monitoring video for each two frames of the monitoring video frames in the multi-frame monitoring video frames.
In this embodiment of the present invention, when performing step S120, the video monitoring server may determine, for each two frames of monitoring video frames in the multiple frames of monitoring video frames included in the to-be-processed monitoring video, a video frame association degree between the two frames of monitoring video frames. And the video frame association degree is used for representing the association relationship closeness degree between the two corresponding monitoring video frames.
Step S130, based on the video frame correlation degree between every two monitoring video frames in the multi-frame monitoring video frames included in the monitoring video to be processed, identifying each monitoring video frame in the multi-frame monitoring video frames included in the monitoring video to be processed to obtain a video frame identification result corresponding to each monitoring video frame.
In this embodiment of the present invention, when step S130 is executed, the video monitoring server may perform identification processing on each of the multiple frames of surveillance video frames included in the surveillance video to be processed based on the video frame association between every two frames of surveillance video frames in the multiple frames of surveillance video frames included in the surveillance video to be processed, so as to obtain a video frame identification result corresponding to each of the multiple frames of surveillance video frames. And the video frame identification result is used for representing the probability of violation information existing in the corresponding monitoring video frame.
Based on this, after the to-be-processed monitoring video sent by the video monitoring terminal device is obtained, the video frame association degree between the two monitoring video frames can be determined for every two monitoring video frames in the multi-frame monitoring video frames included in the to-be-processed monitoring video, then, the video frame identification result corresponding to the monitored video frame of each frame is obtained by performing identification processing on each monitoring video frame based on the determined video frame association degree, namely, the video frame association degree between the monitoring video frames is considered during the identification processing, so that the identification processing effect can be guaranteed, and the problem of poor image identification effect in the prior art is solved.
It is to be understood that, in an alternative implementation, the step S110 in the above example may further include the following steps:
firstly, determining current time information, and judging whether the current time information belongs to preconfigured time interval information, wherein the time interval information is generated based on time management operation carried out by a video monitoring server responding to a corresponding monitoring management user;
secondly, if the current time information belongs to the time interval information, acquiring preset video data volume threshold information, and generating corresponding video monitoring starting notification information based on the video data volume threshold information, wherein the video data volume threshold information is generated based on video data volume management operation of a video monitoring server responding to a corresponding monitoring management user;
then, sending the video monitoring starting notification information to the video monitoring terminal equipment, wherein the video monitoring terminal equipment is used for starting image acquisition on a target monitoring area after receiving the video monitoring starting notification information, and sending the acquired monitored video to be processed with corresponding data volume to the video monitoring server based on the video data volume threshold information carried in the video monitoring starting notification information;
and finally, acquiring the to-be-processed monitoring video acquired and sent by the video monitoring terminal device based on the video monitoring starting notification information.
It is to be understood that, in an alternative implementation, the step S120 in the above example may further include the following steps:
firstly, regarding every two frames of monitoring video frames in a plurality of frames of monitoring video frames included in the monitoring video to be processed, taking the two frames of monitoring video frames as a first target monitoring video frame and a second target monitoring video frame which correspond to each other;
secondly, calculating pixel similarity between the first target surveillance video frame and the second target surveillance video frame, wherein the pixel similarity is determined based on sequence similarity between a first pixel value sequence and a second pixel value sequence, the first pixel value sequence is obtained based on pixel values of pixels in the first target surveillance video frame, and the second pixel value sequence is obtained based on pixel values of pixels in the second target surveillance video frame;
then, determining a pixel value interval to which each pixel point pixel value in the first target surveillance video frame belongs, and obtaining a first pixel identification information sequence based on the pixel value interval to which each pixel point pixel value belongs and a preset pixel value interval-identification information corresponding relation;
then, determining a pixel value interval to which each pixel point pixel value belongs in the second target surveillance video frame, and obtaining a second pixel identification information sequence based on the pixel value interval to which each pixel point pixel value belongs and a preset pixel value interval-identification information corresponding relation;
further, based on the first pixel identification information sequence and the second pixel identification information sequence, determining a pixel identification information similarity between the first target surveillance video frame and the second target surveillance video frame (refer to the following calculation manner regarding the pixel similarity);
and finally, determining video frame association between the first target surveillance video frame and the second target surveillance video frame based on the pixel similarity and the pixel identification information similarity.
It is to be understood that, in an alternative implementation manner, the step of calculating the pixel similarity between the first target surveillance video frame and the second target surveillance video frame in the above example may further include the following steps:
firstly, sequencing based on pixel values of pixel points in a first target surveillance video frame to obtain a first pixel value sequence corresponding to the first target surveillance video frame;
secondly, sequencing based on pixel values of pixel points in the second target surveillance video frame to obtain a second pixel value sequence corresponding to the second target surveillance video frame;
then, determining whether the difference value between pixel point pixel values of corresponding sequence positions between the first pixel value sequence and the second pixel value sequence is smaller than a preset first difference value threshold, and counting the number of corresponding sequence positions of which the difference value between the pixel point pixel values is smaller than the first difference value threshold to obtain the corresponding first sequence position counted number;
finally, the pixel similarity between the first target surveillance video frame and the second target surveillance video frame is obtained based on the first sequence position statistical quantity calculation (for example, the pixel similarity is obtained by calculating the ratio of the first sequence position statistical quantity to the number of sequence positions in the first pixel value sequence or the second pixel value sequence).
It is to be understood that, in an alternative implementation manner, the step of determining a pixel value interval to which a pixel value of each pixel point in the first target surveillance video frame belongs and obtaining the first pixel identification information sequence based on the pixel value interval to which the pixel value of each pixel point belongs and a preset pixel value interval-identification information corresponding relationship in the above example may further include the following steps:
firstly, determining a plurality of pre-divided continuous pixel value intervals (such as 0-50, 51-100, 101-;
secondly, dividing the pixel points of the first target surveillance video frame based on pixel value intervals to obtain at least one first pixel point pixel value string, wherein the pixel value intervals to which the pixel values of any two pixel points in the same first pixel point pixel value string belong are the same, and the pixel value intervals to which the pixel values of any two adjacent first pixel point pixel value strings belong are different;
then, for each first pixel point pixel value string in the at least one first pixel point pixel value string, determining pixel identification information corresponding to the first pixel point pixel value string based on a pixel value interval corresponding to the first pixel point pixel value string and a preset pixel value interval-identification information corresponding relationship (for example, the pixel identification information corresponding to the interval 0-50 may be 1, the pixel identification information corresponding to the interval 51-100 may be 2, and the pixel identification information corresponding to the interval 101-150 may be 3);
and finally, based on the precedence relationship of each first pixel point pixel value string in the at least one first pixel point pixel value string, sequencing the pixel identification information of each first pixel point pixel value string to obtain a corresponding first pixel identification information sequence.
It is to be understood that, in an alternative implementation manner, the step of determining a pixel value interval to which a pixel value of each pixel point in the second target surveillance video frame belongs and obtaining the second pixel identification information sequence based on the pixel value interval to which the pixel value of each pixel point belongs and a preset pixel value interval-identification information corresponding relationship in the above example may further include the following steps:
firstly, determining a plurality of continuous pixel value intervals which are divided in advance, and determining a pixel value interval to which each pixel point pixel value in the second target surveillance video frame belongs;
secondly, dividing the pixel points of the second target surveillance video frame based on pixel value intervals to obtain at least one second pixel point pixel value string, wherein the pixel value intervals to which any two pixel point pixel values in the same second pixel point pixel value string belong are the same, and the pixel value intervals to which the pixel point pixel values in any two adjacent second pixel point pixel value strings belong are different;
then, for each second pixel point pixel value string in the at least one second pixel point pixel value string, determining pixel identification information (as described above) corresponding to the second pixel point pixel value string based on the pixel value interval corresponding to the second pixel point pixel value string and a preset pixel value interval-identification information corresponding relationship;
and finally, based on the precedence relationship of each second pixel point pixel value string in the at least one second pixel point pixel value string, sequencing the pixel identification information of each second pixel point pixel value string to obtain a corresponding second pixel identification information sequence.
It is to be understood that, in an alternative implementation manner, the step of determining the video frame association degree between the first target surveillance video frame and the second target surveillance video frame based on the pixel similarity degree and the pixel identification information similarity degree in the above example may further include the following steps:
firstly, extracting each pixel point pixel value belonging to a preset target pixel value interval from the first target surveillance video frame, and sequencing the extracted pixel point pixel values belonging to the target pixel value interval according to a preset sequence in the first target surveillance video frame to obtain a first target pixel point pixel value sequence corresponding to the first target surveillance video frame;
secondly, extracting each pixel point pixel value belonging to a preset target pixel value interval from the second target surveillance video frame, and sequencing the extracted pixel point pixel values belonging to the target pixel value interval according to a preset sequence in the second target surveillance video frame to obtain a second target pixel point pixel value sequence corresponding to the second target surveillance video frame;
then, determining whether the difference value between pixel point pixel values of corresponding sequence positions between the first target pixel point pixel value sequence and the second target pixel point pixel value sequence is smaller than a preset second difference threshold value, counting the number of corresponding sequence positions of which the difference value between the pixel point pixel values is smaller than the second difference threshold value to obtain a corresponding first sequence position statistical number, and counting the average value of the number of sequence positions in the first target pixel point pixel value sequence and the second target pixel point pixel value sequence to obtain a corresponding second sequence position statistical number;
then, based on the first sequence position statistical quantity and the second sequence position statistical quantity, calculating to obtain a target pixel point pixel value similarity between the first target surveillance video frame and the second target surveillance video frame (for example, calculating a ratio of the first sequence position statistical quantity to the second sequence position statistical quantity to obtain a corresponding target pixel point pixel value similarity);
and finally, performing fusion processing (such as weighted summation calculation) based on the pixel similarity, the pixel identification information similarity and the target pixel point pixel value similarity to obtain the video frame association degree between the first target surveillance video frame and the second target surveillance video frame.
It will be appreciated that in an alternative implementation, step S130 in the above example may further include the following steps:
firstly, clustering multi-frame monitoring video frames included in the monitoring video to be processed based on the video frame association degree between every two monitoring video frames in the multi-frame monitoring video frames included in the monitoring video to be processed to obtain at least one video frame cluster set corresponding to the monitoring video to be processed, wherein each video frame cluster set comprises at least one monitoring video frame;
secondly, for each video cluster set in the at least one video cluster set, performing behavior recognition processing on each frame of surveillance video frame included in the video cluster set based on the same behavior recognition model to obtain a video frame recognition result corresponding to each frame of surveillance video frame (wherein, the surveillance video frames of different video cluster sets can be recognized based on different behavior recognition models, so that the accuracy of the video frame recognition results corresponding to the surveillance video frames in the same video cluster set can be ensured to be consistent, and the subsequent application is facilitated, and the behavior recognition model can be a neural network model obtained based on the training of the prior art, for example, the probability of illegal behavior information existing in the surveillance video frame is determined by calculating the similarity between the standard video frame with the illegal behavior information, i.e., the higher the similarity, the greater the probability).
It is understood that in an alternative implementation, the step S200 in the above example may further include the following steps:
firstly, classifying the multiple monitoring video frames based on a video frame identification result of each monitoring video frame in the multiple monitoring video frames to obtain at least one corresponding video frame classification set, wherein each video frame classification set comprises at least one monitoring video frame;
secondly, determining video frame correlation relation information among the multiple monitoring video frames based on the at least one video frame classification set.
It is to be understood that, in an alternative implementation manner, the step of classifying the multiple frames of surveillance video frames based on the video frame identification result of each of the multiple frames of surveillance video frames to obtain the corresponding at least one video frame classification set in the above example may further include the following steps:
firstly, for each monitoring video frame based on the multiple monitoring video frames, determining a probability interval to which the probability represented by the video frame identification result corresponding to the monitoring video frame belongs in a plurality of pre-configured probability intervals, and obtaining a target probability interval to which the monitoring video frame belongs;
secondly, aiming at each target probability interval, constructing a corresponding video frame classification set based on each monitoring video frame corresponding to the target probability interval.
It is to be understood that, in another alternative implementation manner, the step of classifying the multiple frames of surveillance video frames based on the video frame identification result of each frame of surveillance video frame in the multiple frames of surveillance video frames to obtain the corresponding at least one video frame classification set in the foregoing example may also further include the following steps:
firstly, based on the probability of the representation of the video frame identification result of each of the multiple frames of surveillance video frames, performing clustering processing (which may be based on any one of clustering algorithms in the prior art) on the multiple frames of surveillance video frames to obtain at least one video frame cluster corresponding to the multiple frames of surveillance video frames, wherein each of the at least one video frame cluster includes at least one frame of surveillance video frame;
secondly, for each video frame cluster in the at least one video frame cluster, a corresponding video frame classification set is constructed based on each monitoring video frame included in the video frame cluster (namely, one video frame cluster corresponds to one video frame classification set).
It is to be understood that, in an alternative implementation manner, the step of determining video frame correlation relationship information between the multiple monitoring video frames based on the at least one video frame classification set in the above example may further include the following steps:
firstly, aiming at every two monitoring video frames in the multi-frame monitoring video frames, calculating the difference value between the probabilities represented by the video frame identification results corresponding to the two monitoring video frames to obtain the probability difference value corresponding to the two monitoring video frames, and calculating the sum value of the probabilities represented by the video frame identification results corresponding to the two monitoring video frames to obtain the probability sum value corresponding to the two monitoring video frames;
secondly, calculating the ratio of the probability difference value corresponding to the two monitoring video frames to the probability sum value corresponding to the two monitoring video frames aiming at each two monitoring video frames in the multi-frame monitoring video frames to obtain the probability similarity corresponding to the two monitoring video frames;
then, aiming at every two monitoring video frames in the multi-frame monitoring video frames, determining whether the two monitoring video frames belong to the same video frame classification set in the at least one video frame classification set;
then, aiming at every two monitoring video frames in the multi-frame monitoring video frames, if the two monitoring video frames belong to the same video frame classification set in the at least one video frame classification set, determining the probability similarity corresponding to the two monitoring video frames as video frame correlation relation information between the two monitoring video frames;
and finally, aiming at every two monitoring video frames in the multi-frame monitoring video frames, if the two monitoring video frames belong to two different video frame classification sets in the at least one video frame classification set, calculating the average value of the probability similarity corresponding to every two monitoring video frames between the two video frame classification sets to obtain the similarity mean value corresponding to the two video frame classification sets, and fusing based on the probability similarity corresponding to the two monitoring video frames and the similarity mean value corresponding to the two video frame classification sets to obtain the video frame correlation relation information between the two monitoring video frames.
It can be understood that, in an alternative implementation manner, the step of calculating, for each two frames of surveillance video frames in the multiple frames of surveillance video frames in the above example, an average value of probability similarities corresponding to each two frames of surveillance video frames between the two video frame classification sets if the two frames of surveillance video frames belong to two different video frame classification sets in the at least one video frame classification set, to obtain a similarity average value corresponding to the two video frame classification sets, and obtaining video frame correlation information between the two frames of surveillance video frames based on the fusion of the probability similarities corresponding to the two frames of surveillance video frames and the similarity average values corresponding to the two video frame classification sets may further include the following steps:
firstly, aiming at every two monitoring video frames in the multi-frame monitoring video frames, if the two monitoring video frames belong to two different video frame classification sets in the at least one video frame classification set, calculating the average value of the probability similarity corresponding to every two monitoring video frames between the two video frame classification sets to obtain the similarity average value corresponding to the two video frame classification sets;
secondly, aiming at each two frames of monitoring video frames in the multi-frame monitoring video frames, calculating the product between the probability similarity corresponding to the two frames of monitoring video frames and the similarity mean value corresponding to the two video frame classification sets to obtain the video frame correlation information between the two frames of monitoring video frames (namely the obtained product is used as the video frame correlation information).
It is understood that in an alternative implementation, the step S300 in the above example may further include the following steps:
firstly, clustering the multi-frame monitoring video frames based on the video frame correlation relation information among the multi-frame monitoring video frames to obtain at least one video frame set corresponding to the multi-frame monitoring video frames, wherein each video frame set comprises at least one monitoring video frame;
secondly, for each video frame set in the at least one video frame set, sending each monitoring video frame included in the video frame set to the same image storage device for storage (it can be understood that different video frame sets can be stored in different image storage devices, so that subsequent query can be facilitated).
The embodiment of the invention also provides an automatic image recognition and distribution system (as shown in fig. 3), which can be applied to the video monitoring server. The automatic image recognition and distribution system can comprise the following modules:
the monitoring video frame identification module is used for identifying each monitoring video frame in a plurality of monitoring video frames included in the monitoring video to be processed after the monitoring video to be processed sent by the video monitoring terminal equipment is obtained, and obtaining a video frame identification result corresponding to each monitoring video frame, wherein the monitoring video to be processed comprises a plurality of monitoring video frames, the plurality of monitoring video frames are obtained by carrying out image acquisition on a target monitoring area based on the video monitoring terminal equipment, and the video frame identification result is used for representing the probability of illegal behavior information existing in the corresponding monitoring video frame;
the video frame correlation determination module is used for determining video frame correlation information among the multiple monitoring video frames based on the video frame identification result of each monitoring video frame in the multiple monitoring video frames;
and the monitoring video frame distribution module is used for respectively sending the multiple monitoring video frames to corresponding image storage equipment based on the video frame correlation information among the multiple monitoring video frames, wherein the image storage equipment is used for storing the received monitoring video frames.
It is to be understood that, in an alternative implementation, the video frame correlation determination module may be specifically configured to:
classifying the multiple monitoring video frames based on a video frame identification result of each monitoring video frame in the multiple monitoring video frames to obtain at least one corresponding video frame classification set, wherein each video frame classification set comprises at least one monitoring video frame; and determining video frame correlation relation information among the multiple monitoring video frames based on the at least one video frame classification set.
It is to be understood that, in an alternative implementation, the surveillance video frame distribution module may be specifically configured to:
based on the video frame correlation relation information among the multiple monitoring video frames, clustering the multiple monitoring video frames to obtain at least one video frame set corresponding to the multiple monitoring video frames, wherein each video frame set comprises at least one monitoring video frame; and aiming at each video frame set in the at least one video frame set, sending each monitoring video frame included in the video frame set to the same image storage device for storage.
In summary, according to the automatic image recognition and distribution system and method provided by the present invention, after each of the multiple frames of surveillance video frames included in the obtained surveillance video to be processed is recognized and processed to obtain the video frame recognition result corresponding to each frame of surveillance video frame, the video frame correlation information between the multiple frames of surveillance video frames may be determined based on the video frame recognition result of each frame of surveillance video frame, so that the multiple frames of surveillance video frames may be respectively sent to the corresponding image storage devices based on the video frame correlation information between the multiple frames of surveillance video frames, that is, the reliability and rationality of the image storage devices in controlling and controlling the distribution between the surveillance video frames are achieved, thereby improving the problem of poor control effect of the surveillance video frames in the prior art.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An automatic image recognition and distribution method is applied to a video monitoring server, the video monitoring server is in communication connection with a video monitoring terminal device, and the automatic image recognition and distribution method comprises the following steps:
after a to-be-processed monitoring video sent by the video monitoring terminal equipment is obtained, identifying each monitoring video frame in a plurality of monitoring video frames included in the to-be-processed monitoring video to obtain a video frame identification result corresponding to each monitoring video frame, wherein the to-be-processed monitoring video includes the plurality of monitoring video frames, the plurality of monitoring video frames are obtained by carrying out image acquisition on a target monitoring area based on the video monitoring terminal equipment, and the video frame identification result is used for representing the probability of illegal behavior information existing in the corresponding monitoring video frame;
determining video frame correlation relation information among the multiple monitoring video frames based on a video frame identification result of each monitoring video frame in the multiple monitoring video frames;
and respectively sending the multiple frames of monitoring video frames to corresponding image storage equipment based on the video frame correlation information among the multiple frames of monitoring video frames, wherein the image storage equipment is used for storing the received monitoring video frames.
2. The automatic image recognition distribution method according to claim 1, wherein the step of determining the video frame correlation information among the plurality of surveillance video frames based on the video frame recognition result of each of the plurality of surveillance video frames comprises:
classifying the multiple monitoring video frames based on a video frame identification result of each monitoring video frame in the multiple monitoring video frames to obtain at least one corresponding video frame classification set, wherein each video frame classification set comprises at least one monitoring video frame;
and determining video frame correlation relation information among the multiple monitoring video frames based on the at least one video frame classification set.
3. The automatic image recognition and distribution method according to claim 2, wherein the step of classifying the plurality of surveillance video frames based on the video frame recognition result of each of the plurality of surveillance video frames to obtain the corresponding at least one video frame classification set comprises:
determining a probability interval to which the probability represented by the video frame identification result corresponding to each monitoring video frame in the multi-frame monitoring video frames belongs in a plurality of pre-configured probability intervals aiming at each monitoring video frame in the multi-frame monitoring video frames to obtain a target probability interval to which the monitoring video frame belongs;
and aiming at each target probability interval, constructing a corresponding video frame classification set based on each monitoring video frame corresponding to the target probability interval.
4. The automatic image recognition and distribution method according to claim 2, wherein the step of classifying the plurality of surveillance video frames based on the video frame recognition result of each of the plurality of surveillance video frames to obtain the corresponding at least one video frame classification set comprises:
clustering the multiple frames of monitoring video frames based on the probability represented by the video frame identification result of each frame of monitoring video frames in the multiple frames of monitoring video frames to obtain at least one video frame cluster corresponding to the multiple frames of monitoring video frames, wherein each video frame cluster in the at least one video frame cluster comprises at least one frame of monitoring video frame;
and aiming at each video frame cluster in the at least one video frame cluster, constructing a corresponding video frame classification set based on each monitoring video frame included in the video frame cluster.
5. The automatic image recognition distribution method of claim 2, wherein the step of determining video frame correlation information between the plurality of surveillance video frames based on the at least one video frame classification set comprises:
for each two frames of monitoring video frames in the multi-frame monitoring video frames, calculating the difference value between the probabilities represented by the video frame identification results corresponding to the two frames of monitoring video frames to obtain the probability difference value corresponding to the two frames of monitoring video frames, and calculating the sum value of the probabilities represented by the video frame identification results corresponding to the two frames of monitoring video frames to obtain the probability sum value corresponding to the two frames of monitoring video frames;
calculating the ratio of the probability difference value corresponding to the two frames of the surveillance video frames to the probability sum value corresponding to the two frames of the surveillance video frames aiming at each two frames of the multiple frames of the surveillance video frames to obtain the probability similarity corresponding to the two frames of the surveillance video frames;
determining whether the two surveillance video frames belong to the same video frame classification set in the at least one video frame classification set or not aiming at every two surveillance video frames in the multi-frame surveillance video frames;
for each two frames of monitoring video frames in the multi-frame monitoring video frames, if the two frames of monitoring video frames belong to the same video frame classification set in the at least one video frame classification set, determining the probability similarity corresponding to the two frames of monitoring video frames as video frame correlation relation information between the two frames of monitoring video frames;
for each two frames of monitoring video frames in the multiple frames of monitoring video frames, if the two frames of monitoring video frames belong to two different video frame classification sets in the at least one video frame classification set, calculating an average value of probability similarities corresponding to each two frames of monitoring video frames between the two video frame classification sets to obtain a similarity mean value corresponding to the two video frame classification sets, and fusing based on the probability similarities corresponding to the two frames of monitoring video frames and the similarity mean values corresponding to the two video frame classification sets to obtain video frame correlation information between the two frames of monitoring video frames.
6. The automatic image recognition and distribution method according to claim 5, wherein said step of calculating an average value of probability similarities corresponding to each two surveillance video frames between the two video frame classification sets if the two surveillance video frames belong to two different video frame classification sets of the at least one video frame classification set, obtaining a similarity average value corresponding to the two video frame classification sets, and obtaining video frame correlation information between the two surveillance video frames based on the probability similarities corresponding to the two surveillance video frames and the similarity average values corresponding to the two video frame classification sets for fusion, comprises:
for each two frames of monitoring video frames in the multiple frames of monitoring video frames, if the two frames of monitoring video frames belong to two different video frame classification sets in the at least one video frame classification set, calculating an average value of probability similarities corresponding to each two frames of monitoring video frames between the two video frame classification sets to obtain a similarity average value corresponding to the two video frame classification sets;
and aiming at every two monitoring video frames in the multi-frame monitoring video frames, calculating the product between the probability similarity corresponding to the two monitoring video frames and the similarity mean value corresponding to the two video frame classification sets to obtain the video frame correlation relation information between the two monitoring video frames.
7. The automatic image recognition distribution method according to any one of claims 1 to 6, wherein the step of respectively sending the plurality of surveillance video frames to corresponding image storage devices based on the video frame correlation information among the plurality of surveillance video frames comprises:
based on the video frame correlation relation information among the multiple monitoring video frames, clustering the multiple monitoring video frames to obtain at least one video frame set corresponding to the multiple monitoring video frames, wherein each video frame set comprises at least one monitoring video frame;
and aiming at each video frame set in the at least one video frame set, sending each monitoring video frame included in the video frame set to the same image storage device for storage.
8. The utility model provides an automatic image identification distribution system which characterized in that is applied to the video monitoring server, video monitoring server communication connection has video monitoring terminal equipment, automatic image identification distribution system includes:
the monitoring video frame identification module is used for identifying each monitoring video frame in a plurality of monitoring video frames included in the monitoring video to be processed after the monitoring video to be processed sent by the video monitoring terminal equipment is obtained, and obtaining a video frame identification result corresponding to each monitoring video frame, wherein the monitoring video to be processed comprises a plurality of monitoring video frames, the plurality of monitoring video frames are obtained by carrying out image acquisition on a target monitoring area based on the video monitoring terminal equipment, and the video frame identification result is used for representing the probability of illegal behavior information existing in the corresponding monitoring video frame;
the video frame correlation determination module is used for determining video frame correlation information among the multiple monitoring video frames based on the video frame identification result of each monitoring video frame in the multiple monitoring video frames;
and the monitoring video frame distribution module is used for respectively sending the multiple monitoring video frames to corresponding image storage equipment based on the video frame correlation information among the multiple monitoring video frames, wherein the image storage equipment is used for storing the received monitoring video frames.
9. The automatic image recognition distribution system of claim 8, wherein the video frame correlation determination module is specifically configured to:
classifying the multiple monitoring video frames based on a video frame identification result of each monitoring video frame in the multiple monitoring video frames to obtain at least one corresponding video frame classification set, wherein each video frame classification set comprises at least one monitoring video frame;
and determining video frame correlation relation information among the multiple monitoring video frames based on the at least one video frame classification set.
10. The automatic image recognition distribution system of claim 8, wherein the surveillance video frame distribution module is specifically configured to:
based on the video frame correlation relation information among the multiple monitoring video frames, clustering the multiple monitoring video frames to obtain at least one video frame set corresponding to the multiple monitoring video frames, wherein each video frame set comprises at least one monitoring video frame;
and aiming at each video frame set in the at least one video frame set, sending each monitoring video frame included in the video frame set to the same image storage device for storage.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115082709A (en) * | 2022-07-21 | 2022-09-20 | 济南星睿信息技术有限公司 | Remote sensing big data processing method and system and cloud platform |
| CN115272831A (en) * | 2022-09-27 | 2022-11-01 | 成都中轨轨道设备有限公司 | Transmission method and system for monitoring images of suspension state of contact network |
| CN115908280A (en) * | 2022-11-03 | 2023-04-04 | 广东科力新材料有限公司 | Data processing-based performance determination method and system for PVC calcium zinc stabilizer |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115082709A (en) * | 2022-07-21 | 2022-09-20 | 济南星睿信息技术有限公司 | Remote sensing big data processing method and system and cloud platform |
| CN115272831A (en) * | 2022-09-27 | 2022-11-01 | 成都中轨轨道设备有限公司 | Transmission method and system for monitoring images of suspension state of contact network |
| CN115272831B (en) * | 2022-09-27 | 2022-12-09 | 成都中轨轨道设备有限公司 | Transmission method and system for monitoring images of suspension state of contact network |
| CN115908280A (en) * | 2022-11-03 | 2023-04-04 | 广东科力新材料有限公司 | Data processing-based performance determination method and system for PVC calcium zinc stabilizer |
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