CN111597992B - Scene object abnormity identification method based on video monitoring - Google Patents
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Abstract
A scene object abnormity identification method based on video monitoring is characterized in that each frame of an acquired video is divided into an image, the type and the number of targets appearing in the image are determined, image forming samples are marked, and the images forming sample set of the video is formed as a video, wherein the video is an image sequence project; and performing target extraction on the sample set through a fast-rcnn algorithm, and judging whether the target object in the image has speed abnormality, form abnormality and position abnormality through comparison. According to the method, the problem of inaccurate target extraction caused by shielding can be solved by adopting the fast-rcnn algorithm, and the accuracy of target acquisition is improved so as to improve the accuracy of abnormal behavior detection; the abnormal detection and identification speed is increased, and the calculation efficiency is improved.
Description
Technical Field
The invention relates to the field of behavior identification, in particular to a scene object abnormity identification method based on video monitoring.
Background
The charging and charging can occur in buses and subways and in elevators; some exclusive channels, such as motor vehicle lanes, do not allow bicycles to run, and pedestrians to practice lawns, if these are defined as abnormal behaviors, unnecessary loss can be reduced as long as early warning can be performed in advance, so that a method is needed for judging whether abnormal behaviors exist.
The panoramic monitoring video screen can capture certain scenes, the foreground of a picture needs to be extracted by the existing detection method, then training sample characteristics are extracted, and a normal sample dictionary library is established. And finally, during testing, calculating the similarity between the test sample and the dictionary library to judge whether the test sample is abnormal or not. Therefore, the abnormity judgment is divided into two parts, namely foreground extraction and similarity calculation, so that the steps are complicated and the calculation amount is large; in the past, most algorithms divide image frames into blocks in a grid form or extract foreground targets by a split combination-based method, but the effect on shielding or dense multiple targets is poor.
Disclosure of Invention
The invention aims to provide a scene object abnormity identification method based on video monitoring, so that the problems in the prior art are solved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a scene object abnormity identification method based on video monitoring comprises the following steps:
s1, dividing each frame of a video into continuous images, determining the category and the number of each pixel of each image, and marking the images to form a sample set;
s2, carrying out image segmentation and foreground extraction on the sample set through an rcnn network, training a scene model, and obtaining the class weight of the scene model, wherein the method comprises the following steps:
s201, performing convolution and pooling on the images in the sample set to obtain a feature map in the images;
s202, fusing the feature graph with the shallow feature through upsampling to form feature region division in the image;
s203, subjecting the feature map to convolution kernel of n x 1, and judging the object type in each pixel in the image, wherein n represents the number of the types of the object types contained in the image;
s3, according to the image sequence of the video, combining with the scene model in the step S2, calculating the optical flow of a target object in the image to obtain the speed characteristic of the target object;
s4, obtaining a gray scale image and an edge image of the image so as to obtain morphological characteristics of the target object;
s5, obtaining the position characteristics of the target parent body through the action track of the target object in the image in the step S2;
s6, forming a real-time sample from the video acquired in real time through the step S1, introducing the real-time sample into the scene model trained in the step S2, carrying out image segmentation and type judgment, obtaining the speed characteristic, the morphological characteristic and the position characteristic of the target object in the video acquired in real time through the steps S3-S5, and judging whether the target object in the video is abnormal or not in real time.
Preferably, the samples in the sample set in step S1 are divided into positive samples and negative samples; the positive sample is a sample without a pedestrian, and the negative sample is a sample with a pedestrian.
Preferably, the training of the scene model in step S2 includes two parts, namely, training and testing, that is, the sample set in step S1 is divided into a training set and a testing set, the scene model is trained through the training set, the categories in the image are divided, and then the scene model after the training set is tested through the testing set, so as to obtain a more accurate category detection position.
Preferably, the detection target of the speed feature abnormality is: in the same scene, the speed characteristics of the target object acquired in real time and the conventional speed characteristics have a larger difference.
Preferably, the detection target of the morphological feature abnormality is: and when the target is the same, the form of the target object acquired in real time is different from the conventional form.
Preferably, the detection target of the position feature abnormality is: and in the same scene, acquiring the target object which never appears in the corresponding scene in real time.
The beneficial effects of the invention are: the invention discloses a scene object abnormity identification method based on video monitoring, which adopts a fast-rcnn algorithm to solve the problem of inaccurate target extraction caused by shielding, improves the accuracy of target acquisition and improves the accuracy of abnormal behavior detection; the foreground extraction is carried out in advance through the rcnn network, so that the extracted foreground can be directly applied as comparison when the judgment of morphological abnormality and position abnormality is carried out, and the abnormality detection and identification speed is increased; in addition, the similarity between the normal sample and the morphological abnormality is not required to be calculated when the morphological abnormality is detected, so that the calculation efficiency is improved; the rcnn network is combined with the optical flow, so that objects with abnormal speed can be detected in time, prompt and response can be carried out in time, the occurrence of accidents is further reduced, and unnecessary loss is reduced.
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FIG. 1 is a block diagram of identification of scene object anomalies.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are given by way of illustration only.
A scene object abnormity identification method based on video monitoring is characterized in that each frame of an acquired video is divided into an image, the type and the number of targets appearing in the image are determined, image forming samples are marked, and the images forming sample set of the video is formed as a video, wherein the video is an image sequence project; and performing target extraction on the sample set through a fast-rcnn algorithm, and judging whether the target object in the image has speed abnormality, form abnormality and position abnormality through comparison. The above identification block diagram of the scene object abnormality is shown in fig. 1, and the identification method of the object abnormality includes the following steps:
s1, dividing the video into images with sequences, confirming the types and the number of objects in each pixel of the images, marking, recording the number of the types of the objects in each image as n, and taking the marked images as samples to form a sample set, wherein the images have positive samples of pedestrians and have no negative samples of pedestrians; dividing the sample set into a training set and a testing set, wherein the training set and the testing set respectively comprise the positive samples and the negative samples;
s2, performing image segmentation and foreground extraction on the sample set by using an rcnn network; the method comprises the following steps:
s201, performing convolution and pooling on the images in the sample set to obtain a feature map in the images;
s202, fusing the feature graph with the shallow feature through upsampling to form feature region division in the image;
s203, subjecting the feature map to convolution kernel of n x 1, and judging the object type in each pixel in the image;
by adopting the image segmentation and the foreground extraction, firstly, the training set is utilized to execute the steps S201-S203 to train a scene model, and then the test set is utilized to execute the steps S201-S203 to test the scene model, so as to obtain the more accurate detection position of the object type in the scene;
s3, calculating the optical flow of the target object based on the object types obtained by the segmentation in the step S2 to obtain the speed of the target object, further obtaining the average kinetic energy of the target object, and obtaining the speed characteristic of the target object by combining MHPF characteristics;
s4, obtaining a gray scale image and an edge image of the target object on the basis of the object type of the feature image obtained in the step S2 to obtain the morphological feature of the SIRP target object;
and S5, obtaining the position characteristics of all object types in the image according to the step S2, and judging the position characteristics of the object types by taking the extracted foreground as a background.
S6, forming a real-time sample from the acquired video through the step S1, introducing the real-time sample into the trained scene model in the step S2, carrying out image segmentation and type judgment, obtaining the speed characteristic, the morphological characteristic and the position characteristic of the target object in the video acquired in real time through the steps S3-S5, and judging whether the target object in the video has speed abnormality, morphological abnormality and position abnormality or not in real time;
the basis for judging the speed abnormity is as follows: in the same scene, judging whether the speed of the moving object acquired in real time is greatly different from the conventional speed or not; the basis for judging the morphological abnormality is as follows: aiming at the same object type, judging whether the object has abnormal morphological characteristics; the judgment basis of the position abnormity is as follows: whether or not there are object classes that never appeared in the same scene.
Examples
In this embodiment, the method for identifying scene object anomalies based on video monitoring is applied to a lawn, 6 types of objects including flowers, grasses, trees, birds, pedestrians and fountains are detected in the lawn, and when image segmentation and foreground extraction are performed through an rcnn network, the extracted feature maps are subjected to 6 × 1 convolution sum, and distribution positions corresponding to the types of the objects are integrated together; the average speed of various objects moving in the lawn is calculated. When the speed of a moving object in the video acquired in real time is greatly different from the average speed; collecting the appearance of a certain object and the appearance of an object of a corresponding type in the foreground, wherein the object does not appear or rarely appears; when objects which do not belong to the object types in the scene appear in the lawn, the scene objects are judged to be abnormal.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention discloses a scene object abnormity identification method based on video monitoring, which adopts a fast-rcnn algorithm to solve the problem of inaccurate target extraction caused by shielding, improves the accuracy of target acquisition and improves the accuracy of abnormal behavior detection; the foreground extraction is carried out in advance through the rcnn network, so that the extracted foreground can be directly applied as comparison when the judgment of morphological abnormality and position abnormality is carried out, and the abnormality detection and identification speed is increased; in addition, the similarity between the morphological abnormality detection method and a normal sample is not required to be calculated when morphological abnormality detection is carried out, so that the calculation efficiency is improved; the rcnn network is combined with the optical flow, so that objects with abnormal speed can be detected in time, prompt and response can be carried out in time, the occurrence of accidents is further reduced, and unnecessary loss is reduced.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.
Claims (6)
1. A scene object abnormity identification method based on video monitoring is characterized by comprising the following steps:
s1, dividing each frame of a video into continuous images, determining the category and the number of each pixel of the images, and marking the images to form a sample set;
s2, carrying out image segmentation and foreground extraction on the sample set through an rcnn network, training a scene model, and obtaining the class weight of the scene model, wherein the method comprises the following steps:
s201, performing convolution and pooling on the images in the sample set to obtain a feature map in the images;
s202, fusing the feature graph with the shallow feature through upsampling to form feature region division in the image;
s203, the feature map is subjected to convolution kernel of n x 1, and the object type existing in each pixel in the image is judged, wherein n represents the number of the object type in the image;
s3, according to the image sequence of the video, combining with the scene model in the step S2, calculating the optical flow of a target object in the image to obtain the speed characteristic of the target object;
s4, obtaining a gray scale image and an edge image of the image so as to obtain morphological characteristics of the target object;
s5, obtaining the position characteristics of the target parent body through the action track of the target object in the image in the step S2;
s6, forming a real-time sample from the video acquired in real time through the step S1, introducing the real-time sample into the scene model trained in the step S2, carrying out image segmentation and type judgment, obtaining the speed characteristic, the morphological characteristic and the position characteristic of the target object in the video acquired in real time through the steps S3-S5, and judging whether the target object in the video is abnormal or not in real time.
2. The method for identifying scene object abnormality based on video monitoring according to claim 1, wherein samples in the sample set in step S1 are divided into positive samples and negative samples; the positive sample is a sample without a pedestrian, and the negative sample is a sample with a pedestrian.
3. The video monitoring-based scene object abnormality recognition method according to claim 1, wherein the training of the scene model in step S2 includes two parts, namely training and testing, i.e., the sample set in step S1 is divided into a training set and a testing set, the scene model is trained through the training set first, the categories in the image are divided, and then the scene model after the training set is tested through the testing set, so as to obtain a more accurate category detection position.
4. The method for identifying scene object abnormality based on video surveillance according to claim 1, wherein the detection targets of the speed feature abnormality are: in the same scene, the speed characteristics of the target object acquired in real time are different from the conventional speed characteristics.
5. The method for identifying scene object abnormality based on video surveillance according to claim 1, wherein the detection targets of morphological feature abnormality are: and when the target is the same, the form of the target object acquired in real time is different from the conventional form.
6. The method for identifying scene object abnormality based on video surveillance according to claim 1, wherein the detection targets of the position feature abnormality are: and in the same scene, acquiring the target object which never appears in the corresponding scene in real time.
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