CN113762007A - Abnormal behavior detection method based on appearance and action characteristic double prediction - Google Patents

Abnormal behavior detection method based on appearance and action characteristic double prediction Download PDF

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CN113762007A
CN113762007A CN202011263894.5A CN202011263894A CN113762007A CN 113762007 A CN113762007 A CN 113762007A CN 202011263894 A CN202011263894 A CN 202011263894A CN 113762007 A CN113762007 A CN 113762007A
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陈洪刚
李自强
王正勇
何小海
刘强
吴晓红
熊书琪
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Abstract

The invention discloses an abnormal behavior detection method based on appearance and action characteristic double prediction, and relates to the field of computer vision and artificial intelligence. The method comprises the following steps: (1) sequentially reading a video frame sequence, calculating the inter-frame difference of adjacent images, and acquiring the video frame sequence with fixed length and a corresponding frame difference image sequence; (2) by utilizing a double-current network model introduced into a memory enhancement module, extracting special appearance and action characteristics belonging to normal behaviors through appearance and action sub-networks respectively, and predicting a video frame image and a frame difference image; (3) adding and fusing the predicted video frame and the frame difference image to obtain a final predicted video frame; (4) and obtaining the frame abnormality score by evaluating the action and appearance characteristics extracted by the memory enhancement module and the quality of the final prediction image. The invention adopts a deep learning method based on a prediction model, can effectively detect the video frames containing abnormal behaviors, and improves the accuracy of abnormal detection.

Description

Abnormal behavior detection method based on appearance and action characteristic double prediction
Technical Field
The invention relates to an abnormal behavior detection method based on appearance and action characteristic double prediction, and belongs to the field of computer vision and security monitoring.
Background
Abnormal behavior detection is a technique in the field of computer vision, and the purpose of abnormal behavior detection is to detect the existence of abnormal behavior in a video. In recent times, public safety is getting more and more concerned, a great amount of monitoring equipment is deployed at each place, thereby generating huge amount of video resources, and it is extremely difficult to pay attention to each monitoring picture in real time by manpower, and a great amount of manpower resources are consumed. By using the abnormal behavior detection algorithm, the abnormal behavior in the monitoring video can be detected and a warning can be given out in time, so that the labor cost can be greatly reduced, and the efficiency is improved. The abnormal behavior detection has wide application prospect in the fields of video monitoring, intelligent security, transportation and the like.
For the abnormal behavior detection of videos, due to the low occurrence rate of abnormal behaviors and the difficult reason of data collection, most current methods adopt a semi-supervised learning method which only uses normal videos for training, and a reconstruction or prediction based method becomes a mainly used method due to the good detection effect. The method reconstructs an input frame or predicts the next frame by inputting a plurality of continuous frames of video to a self-encoder network or generating a countermeasure network, and judges whether the video is abnormal or not by judging whether the video is reconstructed or predicted. Although this type of method achieves good results, the following problems are still faced: (1) abnormal behaviors can be classified into appearance, action or both, and the current reconstruction and prediction methods fully utilize appearance and action information; (2) the normal behaviors have diversity, the network cannot correctly learn the special characteristics of the normal sample due to the complex background and the like, and in addition, the reconstruction or prediction effect of the abnormal sample can be good due to the strong generating capacity of the convolutional neural network, so that the final abnormal detection accuracy is influenced.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides an abnormal behavior detection method based on appearance and action characteristic double prediction, and aims to design a double-flow network structure which comprises a memory enhancement module and is used for predicting appearance and action characteristics, so that an abnormal video frame can obtain a larger prediction error, and the accuracy of abnormal behavior detection is improved.
The invention adopts the following technical scheme: an abnormal behavior detection method based on appearance and action characteristic double prediction comprises the following steps:
(1) sequentially reading video frames, calculating the inter-frame difference of adjacent images, and acquiring a video frame sequence with a fixed length and a corresponding frame difference image sequence;
(2) by utilizing a double-current network model introduced into a memory enhancement module, extracting special appearance and action characteristics belonging to normal behaviors through an appearance sub-network and an action sub-network respectively, and predicting a video frame image and a frame difference image;
(3) adding and fusing the predicted video frame and the frame difference image to obtain a final predicted video frame;
(4) and obtaining the abnormal score of the frame by measuring the action and appearance characteristics extracted by the memory enhancement module and the quality of the final prediction image.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention simultaneously uses the video frame sequence and the RGB frame difference image sequence as input to be sent into a double-current convolution self-encoder network for prediction, compared with the prior method which uses a light flow graph to extract action characteristics, the invention can reduce the network complexity and the calculated amount by using the frame difference image;
2. the invention improves the network structure of the encoder and the decoder in the self-coding network, so that the characteristics can be better extracted, and the image prediction quality can be improved;
3. the method adds the memory enhancement module, better learns the characteristics of the normal sample, enhances the robustness of the network and enables the abnormal video to obtain a higher abnormal score;
4. according to the method, the quality of the prediction image is considered, and the feature similarity score of the extracted sample features and the feature similarity score of the normal behavior features are used as evaluation basis, so that the effect of abnormality detection is effectively improved, and the false detection rate is reduced.
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FIG. 1 is a flow chart of the abnormal behavior detection method of the present invention;
FIG. 2 is a network architecture diagram of the present invention for anomalous behavior detection based on dual prediction of appearance and motion characteristics;
fig. 3 is a block diagram of the upsampling and downsampling modules in the encoder and decoder of the present invention.
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 invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1-2, an abnormal behavior detection method based on appearance and motion feature dual prediction includes the following steps:
(1) sequentially reading video frames, calculating the inter-frame difference of adjacent images, and acquiring a video frame sequence with a fixed length and a corresponding frame difference image sequence;
(2) by utilizing a double-current network model introduced into a memory enhancement module, extracting special appearance and action characteristics belonging to normal behaviors through an appearance sub-network and an action sub-network respectively, and predicting a video frame image and a frame difference image;
(3) adding and fusing the predicted video frame and the frame difference image to obtain a final predicted video frame;
(4) and obtaining the frame abnormality score by measuring the action and appearance characteristics extracted by the memory enhancement module and the quality of the final prediction image.
The detailed steps are as follows:
step 1: fixed length video frames and frame difference maps are acquired. A section of video stream is obtained from a fixed camera, after the video is subjected to framing processing, a continuous video frame sequence with the fixed length of t is selected, wherein the first t-1 frame image is directly sent into an appearance sub-network. For a video stream of a fixed camera, a background image I of the video can be acquired by an OpenCV methodBThen subtracting I from t frame RGB video imageBObtaining a foreground image I 'without background noise'1,I′2,…,I′tAnd finally, the foreground image is takenSubtracting the previous frame from the next frame of the sequence to obtain t-1 continuous frame difference image sequences X required by the action sub-network1,X2,…,Xt-1。
Step 2: and respectively sending the video frames and the frame difference images with fixed lengths into a double-current network introduced into a memory enhancement module for prediction to generate predicted video frames and RGB frame difference images.
For the network architecture, as shown in fig. 2, the network is composed of two structurally identical sub-networks of self-encoders, which are widely used for the tasks of feature extraction and reconstruction and prediction of images. Taking the visiting sub-network as an example, further explanation is: the sub-networks are in turn formed by an encoder EaMemory enhancing module MaAnd decoder DaAre cascaded. The encoder and the decoder are connected in a skip-connection mode in a feature layer with the same resolution, and the memory enhancement module performs normal sample feature enhancement on the features extracted by the encoder and then sends the features to the decoder for reconstruction. For an encoder and a decoder, the invention improves an up-sampling layer and a down-sampling layer of the encoder and the decoder, as shown in fig. 3, the improved up-sampling module and the down-sampling module both adopt a residual error-like structure, and two branches of the down-sampling module respectively pass convolution operation and maximum pooling operation of different kernel functions; the up-sampling module adopts deconvolution operations of convolution kernels with different sizes. The improved convolution kernel has richer acquired information and can extract more effective semantic features. Setting input appearance sub-network input to I1,I2,…,ItThrough an encoder EaDown-sampling extraction of deep features Z about image scenes, target appearance information, and the likeaThen, the memory enhancing module MaFor feature ZaPerforming memory enhancement on a normal sample to obtain enhanced characteristic Z'aDecoder DaInput Z'aPredicting to obtain the t +1 th frame
Figure BDA0002775501630000031
The calculation method is shown in formula (1):
Figure BDA0002775501630000041
in the above formula
Figure BDA0002775501630000042
Respectively representing encoders EaMemory enhancing module MaAnd decoder DaThe parameter (c) of (c).
The memory enhancing module is specifically described as follows:
the module comprises a memory item for storing M normal sample feature vectors in local; in the training phase, the encoder feeds all the features of the extracted normal samples into the module, which extracts the M features that best characterize the normal samples and stores them locally. The function of the module is realized by reading and updating two operations.
For read operations, which are used for reconstruction of the decoder in order to generate enhanced features, they exist in the training and testing phase of the network. The reading operation steps are as follows: for the output feature z of the encoder, calculating the cosine similarity between z and the storage feature p in the memory term, where the calculation formula is shown in (2):
Figure BDA0002775501630000043
where k and m are indices of features z and p, respectively, for s (z)k,pm) Applying softmax function to obtain read weight omegak,mThe calculation formula is as shown in (3):
Figure BDA0002775501630000044
applying the calculated correspondence weight ω to the memory item feature pk,mObtaining features of enhanced memory
Figure BDA0002775501630000048
The calculation method is as follows:
Figure BDA0002775501630000045
the updating operation only exists in the training stage and is used for learning the characteristic features of the normal sample, firstly, the cosine similarity is calculated by using the formula (1), and then, the updating weight v is calculatedm,kThe calculation method is as the formula (5):
Figure BDA0002775501630000046
the calculation method of the updated local memory is as the formula (6):
Figure BDA0002775501630000047
in order for the memory items to really remember the characteristics of the normal samples, the module introduces a characteristic compression loss LcAnd characteristic separation loss LsTwo loss functions. Characteristic compression loss LcAs shown in equation (7):
Figure BDA0002775501630000051
in the above formula pτRepresenting all memory items with zkThe one with the highest similarity.
Characteristic separation loss LsThe calculation method of (2) is shown in equation (9):
Figure BDA0002775501630000052
in the above formula, τ and γ respectively represent ω in the formula (1)k,mThe value of index m at the time of the maximum value and the second largest value is taken. And step 3: predicting the predicted video frames from the appearance and motion sub-networks by step 2
Figure BDA0002775501630000053
Sum RGB frame difference map
Figure BDA0002775501630000054
Adding the two prediction images to obtain the final t +1 frame video frame of the network
Figure BDA0002775501630000055
And 4, step 4: the method for calculating the abnormality score specifically includes:
first, the t +1 th frame is calculated
Figure BDA0002775501630000056
With real frame It+1The peak signal-to-noise ratio (PSNR) is calculated as shown in equation (10):
Figure BDA0002775501630000057
wherein N represents the t +1 th frame image It+1The number of all pixels.
Secondly, calculating each output characteristic z of the appearance sub-network and the motion sub-network encoderkMemory item characteristic p of memory enhancement moduleτThe L2 distance of (a) is used as the feature similarity score of the two sub-networks, and the calculation method is shown in formula (11):
Figure BDA0002775501630000058
wherein τ is and zkThe index of the memory item features with the maximum similarity;
finally, after the three scores are normalized to [0,1], the weight of each score is balanced by a hyper-parameter beta, and the calculation method is shown as a formula (12):
Figure BDA0002775501630000059
in the formula
Figure BDA00027755016300000510
D′a(za,pa) And D'm(zm,pm) And respectively representing the normalized PSNR, the appearance feature similarity score and the action feature similarity score.
In order to verify the effectiveness of the method, the method uses three common data sets of Avenue, UCSD-ped2 and ShanghaiTech commonly used in the field of video abnormal behavior detection to train and test. Four abnormal behavior detection methods based on deep learning are selected as comparison methods, and the method specifically comprises the following steps:
the method comprises the following steps: the methods proposed by Abati et al, references "D.Abati, A.Porrello, S.Calderara, and R.Cucchiaara," tension space autogiration for novel detection, "in Proceedings of the IEEE Conference on Computer Vision and Pattern registration 2019, pp.481-490".
The method 2 comprises the following steps: nguyen et al, references "T. -. N.Nguyen and J.Meuner," analysis detection in video sequence with application-correlation, "in Proceedings of the IEEE International Conference on Computer Vision,2019, pp.1273-1283"
The method 3 comprises the following steps: liu et al, references "W.Liu, W.Luo, D.Lian, and S.Gao," Future frame prediction for analog detection-a new base, "in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018, pp.6536-6545"
The method 4 comprises the following steps: the methods proposed by Gong et al, references "D.Gong et al", "Memory-aided depth auto encoder for unsupervised analysis," in Proceedings of the IEEE International Conference on Computer Vision,2019, pp.1705-1714 "
As shown in table 1, the method provided by the present invention uses AUC as an evaluation index on three data sets, and compared with the other four methods, the accuracy of the method identification is greatly superior.
TABLE 1 comparison with other methods evaluation index (AUC)
Figure BDA0002775501630000061
Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present invention, but not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. An abnormal behavior detection method based on appearance and action characteristic double prediction is characterized by comprising the following steps:
(1) sequentially reading video frames, calculating the inter-frame difference of adjacent images, and acquiring a video frame sequence with a fixed length and a corresponding frame difference image sequence;
(2) by utilizing a double-current network model introduced into a memory enhancement module, extracting special appearance and action characteristics belonging to normal behaviors through an appearance sub-network and an action sub-network respectively, and predicting a video frame image and a frame difference image;
(3) adding and fusing the predicted video frame and the frame difference image to obtain a final predicted video frame;
(4) and obtaining the frame abnormality score by measuring the action and appearance characteristics extracted by the memory enhancement module and the quality of the final prediction image.
2. The abnormal behavior detection method based on appearance and motion feature double prediction as claimed in claim 1, wherein the frame difference map calculation method in step (1) is as follows:
for each video segment, firstly, extracting a background image of the video segment; secondly, for the video frame sequence with the fixed length of t, subtracting a background image to obtain a foreground target image with the background removed; and finally, subtracting the adjacent frames to obtain a frame difference image sequence with the length of t-1.
3. The abnormal behavior detection method based on appearance and action feature double prediction as claimed in claim 1, wherein the double-current network structure with the memory enhancement module introduced in step (2) includes two convolutional neural networks including an appearance sub-network and an action sub-network, and the appearance sub-network and the action sub-network are composed of self-encoder networks with the same structure.
4. The abnormal behavior detection method based on appearance and action feature double prediction as claimed in claims 1 and 3, characterized in that the self-encoder network is composed of an encoder, a decoder and a memory enhancement module; the memory enhancement module is cascaded between the encoder and the decoder.
5. The abnormal behavior detection method based on appearance and action feature double prediction as claimed in claim 4, characterized in that, the network structure of the encoder and decoder;
the encoder and the decoder respectively comprise three down-sampling layers and three up-sampling layers; the down-sampling layer adopts a residual error structure, and the two branches respectively adopt maximum pooling and convolution to reduce the resolution and increase the number of channels; the two branches of the upper sampling layer sample deconvolution of convolution kernels with different sizes to improve resolution and reduce the number of channels; the encoder and decoder use a jump connection at the feature layer of the same resolution.
6. The method according to claim 1, wherein the memory enhancement module in step (2) includes M memory entries for storing the normal sample feature vectors locally; the memory enhancement module is divided into two operations of reading and updating;
the reading operation has a network training and testing stage at the same time; the reading operation steps are as follows: for the output feature z of the encoder, calculating the cosine similarity between z and the storage feature p in the memory term, wherein the calculation formula is shown as (1):
Figure FDA0002775501620000021
where k and m are indices of features z and p, respectively, for s (z)k,pm) Applying softmax function to obtain read weight omegak,mThe calculation formula is as (2):
Figure FDA0002775501620000022
applying the calculated correspondence weight ω to the memory item feature pk,mObtaining features of enhanced memory
Figure FDA00027755016200000212
The calculation method is as follows:
Figure FDA0002775501620000023
the updating operation only exists in a training stage, firstly cosine similarity is calculated by using a formula (1), and then updating weight v is calculatedm,kThe calculation method is as the formula (4):
Figure FDA0002775501620000024
the calculation method of the updated local memory is as the formula (5):
Figure FDA0002775501620000025
the updated memory entries will be stored locally and used in the read operations of training and testing.
7. The abnormal behavior detection method based on appearance and motion feature double prediction as claimed in claim 1, wherein the method for obtaining the final predicted video frame in step (3) comprises:
inputting continuous t-1 video frame images into appearance sub-network to predict to obtain t-th frame
Figure FDA0002775501620000026
Inputting the continuous t-1 frame difference diagrams into the action sub-network to predict the t-th frame difference diagram
Figure FDA0002775501620000027
Finally will be
Figure FDA0002775501620000028
And
Figure FDA0002775501620000029
adding and fusing to obtain the t +1 th frame
Figure FDA00027755016200000210
8. The abnormal behavior detection method based on the dual prediction of appearance and motion features as claimed in claim 1, wherein in the step (4), the abnormal score is calculated by the following method:
(4.1) calculating the t +1 th frame
Figure FDA00027755016200000211
With real frame It+1Peak signal-to-noise ratio (PSNR);
(4.2) calculating the output characteristics z of each of the encoders in the appearance sub-network and the motion sub-network, respectivelykMemory item characteristic p of memory enhancement moduleτThe L2 distance of (a) is used as the feature similarity score of the two sub-networks, and the calculation method is shown in formula (6):
Figure FDA0002775501620000031
wherein τ is and zkThe index of the memory item features with the maximum similarity;
(4.3) normalizing the three scores in the step (4.1) and the step (4.2) to [0,1], and then adding and fusing to obtain a final abnormal score, wherein the higher the score is, the higher the possibility of video frame abnormality is, and the score calculation method is as shown in (7):
Figure FDA0002775501620000032
in the formula
Figure FDA0002775501620000033
D′a(za,pa) And D'm(zm,pm) And respectively representing the normalized PSNR, the appearance feature similarity score and the action feature similarity score.
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