Research On Cluster Analysis And Anomaly Detection Based On Multi-Feature Vehicle Trajectory

With the continuous development of intelligent traffic surveillance technology, behavior analysis and recognition based on the trajectories of moving targets has become a hot topic,in which cluster analysis and anomaly detection are important research contents. Typical trajectory motion patterns of surveillance scene can be automatically obtained by clustering the trajectories of moving targets. Anomaly detection is aimed at detecting automatically abnormal behaviors in the surveillance scene and alarming timely, which is a key step to realize intelligent surveillance. This paper focuses on trajectory cluster analysis and anomaly detection in the field of intelligent traffic surveillance, and makes a thorough study of the problems in these two key technologies. Then practical solutions are proposed by taking full advantage of trajectory feature information. The main work and contributions of this paper are summarized as follows:In cluster analysis, traditional clustering algorithms only use single feature information for clustering, which reduces the accuracy of clustering. To solve this problem, hierarchical clustering algorithm based on multi-feature trajectory is proposed. This method uses Bhattacharyya distance and modified Hausdorff distance based segment interpolation (IMHD)to measure the similarity of motion direction and spatial location among trajectories respectively, and then extracts the trajectory motion patterns by the coarse-to-fine hierarchical clustering. To improve the efficiency of clustering, Laplacian mapping is introduced to reduce the computational complexity and to determine automatically the number of clusters in each layer of the agglomerative hierarchical clustering.In anomaly detection, a new description for abnormal behavior firstly is proposed.According to the different extent and nature that abnormal trajectories deviate from normal patterns, three common anomaly types are defined, which are starting point anomaly, global anomaly and local anomaly. The improved method effectively solves the problems that traditional description methods are not universal and define anomaly types vaguely. After that,anomaly detection method based on multi-feature trajectory is proposed to solve the problem that traditional methods only consider spatial location anomalies while ignoring direction anomalies, or can only detect abnormal trajectories with larger differences while ignoring local sub-segment abnormalities. This method firstly learns distribution patterns of starting location in surveillance scene by GMM model and establishes starting distribution model.Then the classifiers based on the position distance and direction distance are established by learning the location patterns and direction patterns of each normal trajectory classes after clustering,which take the moving window as a basic comparison unit. Finally at anomaly detection stage, combining with the anomaly types defined in this paper, the proposed abnormal detection algorithm based on multi-feature trajectory measures the differences between the tested trajectory and trajectory patterns in starting point distribution, position and direction, and judges whether it is abnormal trajectory. By sliding the moving window,the algorithm realizes the on-line detection for dynamically incremental trajectory data.Finally, clustering algorithm and anomaly detection method proposed in this paper are applied to vehicle trajectory data in real traffic scene. Experimental results show that the proposed methods can extract vehicle motion patterns of traffic scene quickly and accurately,and can automatically detect a variety of common abnormal behaviors. And the two methods are superior to traditional methods at clustering accuracy and abnormal recognition rate respectively.