Event-based large scale surveillance video summarization

Abstract

Recent advances in sensor manufacture and computer vision technologies have simulated the applications of intelligent transportation systems, while a key yet under-addressed issue in these systems is the semantic summarization of large scale surveillance video. The main difficulty of large scale surveillance video summarization arises from the contradiction between the high-degree spatiotemporal redundancies and the limited storage budget. In this paper, we propose a novel approach of large scale surveillance video summarization on the basis of event detection. In the proposed approach, we firstly obtain the trajectories of vehicles and pedestrians in a tracking-by-detection manner, and then detect the abnormal events using the trajectories. Finally, we design a disjoint max-coverage algorithm to generate a summarized sequence with maximum coverage of interested events and minimum number of frames. Compared with traditional key frame-based approaches, our approach enjoys the following favorable features. First, important information can be efficiently extracted from the redundant contents since the approach is event-centric and those interested events contain almost all the important information. Second, abnormal events are successfully detected by combining the Random Forest classifier and the trajectory features. Third, the abnormal events are designed to display, and hence further reduces the compression ratio. Due to the above features, the proposed approach is suitable for different scenarios, ranges from highway to crowded crossings. Experiments on 12 surveillance sequences validate the effectiveness and efficiency of the proposed approach.