A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were gen...A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were generated by an Apriori based frequent patterns mining algorithm and the trajectories were classified by the frequent trajectory patterns generated.In addition,a fuzzy c-means(FCM)based learning algorithm and a mean shift based clustering procedure were used to construct the representation of trajectories.The algorithm can be further used to describe activities and identify anomalies.The experiments on two real scenes show that the algorithm is effective.展开更多
In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the featu...In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.展开更多
基金National High-Tech Research and Development Plan of China(No.2003AA1Z2130)Science and Technology Project of Zhejiang Province of China(No.2005C1100102)
文摘A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were generated by an Apriori based frequent patterns mining algorithm and the trajectories were classified by the frequent trajectory patterns generated.In addition,a fuzzy c-means(FCM)based learning algorithm and a mean shift based clustering procedure were used to construct the representation of trajectories.The algorithm can be further used to describe activities and identify anomalies.The experiments on two real scenes show that the algorithm is effective.
基金The National Natural Science Foundation of China(No.41471371)
文摘In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.