Research On Crowd Abnormal Event Detection In Surveillance Video

The traditional video surveillance system generally adopts the working mode of “video storage plus manual handling”.This mode often has high labor costs and easily makes the monitoring staff fatigue.With the geometric growth of the number of surveillance cameras in recent years,it is not realistic to require supervisors to complete real-time monitoring of every video message.So the intelligent video surveillance system is put forward.By analyzing the video data collected by the surveillance camera,this dissertation puts forward the corresponding algorithm to detect the abnormal events of the crowd in the surveillance video.The main tasks are as follows:(1)It is found that there are some rules when watching the video,the eyeball has a certain degree of attention to the different regions of the video.The supervisor tends to pay attention to each part of the video scene when a normal video is being played,and a balance is reached.But when an abnormal crowd event suddenly occurs,the pedestrians in the scene will immediately have abnormal reactions(behaviors),and the attention of the supervisor will focus immediately on the area where the abnormal event is discovered.According to this characteristic of human visual attention,this dissertation applies saliency information of human visual attention to the detection of abnormal crowd events.Based on FIT theory combined with human visual system and quaternion Fourier transformation,the analysis of the existing image and video saliency detection has been made and a saliency detection model for abnormal crowd event is proposed.The experimental results show that the saliency information generated by our model can be used to detect abnormal crowd events well.(2)The salience detection model proposed in this dissertation uses the moving speed and brightness information of pixels directly.But the detection model of abnormal crowd event based on its saliency information is affected by the brightness of background.In order to reduce the influence,a statistical feature of optical flow and saliency information are combined to detect abnormal crowd events.Because of the statistical feature,the detection model can further improve the accuracy of the detection of abnormal events in the crowd.(3)At present there are many manual features used in abnormal crowd event detection such as optical flow,histogram of optical flow,global optical flow orientation histogram and so on.With the use of deep neural networks in recent years,automatic extraction of high-level features from low-level features becomes a better feature extraction method.As the process of feature extraction from lower level to higher level is very similar to the learning process of human brain,an unsupervised deep neural network(PCANet)is proposed to study low-level features and extract high-level features to detect abnormal crowd events.The experimental results show that the highlevel features can improve the accuracy of detecting abnormal crowd events.(4)In the detection of abnormal crowd events,the optical flow is always a feature that cannot be bypassed,because it is a two-dimensional instantaneous velocity matrix of pixel points,which reflects the motion attributes of the current frame.However,if there is a non-pedestrian movement in the scene,the optical flow cannot distinguish the movement of the pedestrian from the movement of the non-pedestrian,which will cause the movement of the non-pedestrian to be collected as the motion information as well,and which will ultimately affect the detection results of the abnormal crowd events.In order to solve this problem,the optical flow should not be used directly,and pedestrians should be distinguished from non-pedestrians.Therefore,this dissertation proposes to use crowd intensity to represent the pedestrian in the video,and to detect crowd abnormal events according to the change of crowd density.Finally,the experiments show that the crowd density can be used to detect abnormal crowd events,and that the calculation speed is much faster than the model based on optical flow.