A Novel Approach For Global Abnormal Event Detection in Multi-camera Surveillance System
文献类型：会议论文
| 作者 | Huiwen Guo; huan wang; Lixin Chen; Haifei Huang; Xinyu Wu |
| 出版日期 | 2015 |
| 会议名称 | IEEE International Conference on Information and Automation |
| 会议地点 | Lijiang |
| 英文摘要 | In this paper, a novel global abnormal event detection algorithm is proposed for multiple disjoint synchronous camera network. We treat detecting unusual global events as discovering context-incoherent patterns through learning temporal dependencies between distributed local activities observed within and across camera views. Trajectories are firstly extracted using mean-shift approach in each camera view. As local activities are learned by applying clustering algorithm for trajectories, we model global event patterns using a probabilistic graphical model with different nodes representing entry/exit regions from different views and the directed links between nodes encoding their temporal dependencies. A novel two-stage structure learning algorithm is formulated to learn globally optimized temporal dependencies. Modified Dynamic Time Warping is used to learning the links in unobservable regions in camera network. Then, Monte Carlo(MC) algorithm is used to refine the structure and produce a final dependency structure. We validate the effectiveness of the proposed approach using a synthetic data set and videos captured from a camera network installed at a research institute. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/6778]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2015 |
| 推荐引用方式 GB/T 7714 | Huiwen Guo,huan wang,Lixin Chen,et al. A Novel Approach For Global Abnormal Event Detection in Multi-camera Surveillance System[C]. 见:IEEE International Conference on Information and Automation. Lijiang. |
入库方式： OAI收割
来源：深圳先进技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。