Research On Abnormal Activity Detection In Videos

In the domain of computer vision,the abnormal activities detection in videos has been an important research task.It can be widely applied to a lot of scenarios such as the video summary,scene analysis,and monitoring.In this paper,we respectively apply the exploration and research on the abnormal activities detection under the conditions of overlap target and separation target.And the main innovation of this paper includes the design and extraction of feature,the construction of high-level video representation,and the selection of modeling pattern.Around these innovations,this paper proposes the following three kinds of algorithms for abnormal activity detection.1.Abnormal activity detection based on Laplace sparse coding.In the target-overlap videos,it's difficult to detect and track targets.Therefore,this paper utilizes a method based on the local video feature and Laplacian sparse coding to detect the abnormal activities.Firstly,the local video features are extracted in the three dimensional space.Secondly,the Laplacian sparse coding scheme is used to encode local video features to produce the high level video representations.Then,the pooling method such as max pooling is adopted to integrate all code coefficients of a video to one feature vector.Finally,the support vector machine is utilized to classify these feature vectors as abnormal or normal.Experiments on two datasets demonstrate the satisfactory performance of the proposed approach.2.Abnormal activity detection based on nonnegative locality-constraint linear coding.In the videos which contains many overlap targets,the discrimination of local video feature and the performance of the coding scheme have a great influence on the detection of abnormal activities.For example,because the coding scheme is a reconstruction of the local video feature based on the pre-trained over-complete codebook,the inevitable information loss of the coding phase may influence the performance of the proposed approach.In order to further improve the accuracy of abnormal activity detection,this paper proposes another detection framework via the position-based local feature and the nonnegative locality-constrained linear coding.The introduction of the position information makes the local video feature more discriminative.The nonnegative locality-constrained linear coding makes the coding coefficient meet not only sparsity but also locality,which can reduce the reconstruction error and produce more robust and descriptive video representation.Experimental results have indicated more promising performance of the new method on the abnormal activity detection in target-overlap videos.3.Abnormal activity detection based on the trajectory of moving target.For the abnormal activity detection in the target-separation videos,this paper utilizes a method based on the trajectory of moving target.Because the abnormal activity usually occurs with a low frequency in the scenarios such as the crossroad or square,the video length will be very large if we directly model and learn the abnormal pattern.Therefore,this paper utilizes the strategy which models the normal pattern.First of all,the tracking technique is applied to gain the entire trajectories from targets which enter the view field.Then,the speed feature and space information are extracted from these trajectories.After that,the clustering scheme is utilized to produce centers of the normal pattern.For each trajectory to be detected,we calculate the similarity between it and each center.If the trajectory is similar to at least one of all the centers,it is normal.If it is not similar to any center,it is abnormal.Experimental results on two surveillance videos have demonstrated that the proposed method can perform effectively in the target-separation abnormal activity detection.