Name
Title | ||
|---|---|---|
Abnormal visual event detection based on multi‐instance learning and autoregressive integrated moving average model in edge‐based Smart City surveillance |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1002/spe.2701 | SOFTWARE-PRACTICE & EXPERIENCE |
Keywords | DocType | Volume |
abnormal visual event detection,autoregressive integrated moving average model,crowded scene,multi-instance learning,Smart City | Journal | 50.0 |
Issue | ISSN | Citations |
SP5.0 | 0038-0644 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianghua Xu | 1 | 28 | 3.59 |
| LiQiming Liu | 2 | 0 | 0.34 |
| Lingjun Zhang | 3 | 27 | 1.58 |
| Ping Li | 4 | 136 | 11.08 |
| Jinjun Chen | 5 | 3 | 2.07 |