| Affiliation: | 1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China;2. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
Zhejiang Provincial Key Laboratory of Data Storage and Transmission Technology, Hangzhou, China;3. School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, Australia |
| Abstract: | The abnormal visual event detection is an important subject in Smart City surveillance where a lot of data can be processed locally in edge computing environment. Real-time and detection effectiveness are critical in such an edge environment. In this paper, we propose an abnormal event detection approach based on multi-instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places, focusing on real-time and detection effectiveness. We propose an unsupervised method for abnormal event detection by combining multi-instance visual feature selection and the autoregressive integrated moving average model. In the proposed method, each video clip is modeled as a visual feature bag containing several subvideo clips, each of which is regarded as an instance. The time-transform characteristics of the optical flow characteristics within each subvideo clip are considered as a visual feature instance, and time-series modeling is carried out for multiple visual feature instances related to all subvideo clips in a surveillance video clip. The abnormal events in each surveillance video clip are detected using the multi-instance fusion method. This approach is verified on publically available urban surveillance video datasets and compared with state-of-the-art alternatives. Experimental results demonstrate that the proposed method has better abnormal event detection performance for crowded scene of urban public places with an edge environment. |
