自动化学报2016，Vol.42Issue(6)：892-903,12.DOI:10.16383/j.aas.2016.c150729
基于CNN的监控视频事件检测
Surveillance Event Detection Based on CNN
王梦来 1李想 1陈奇 1李澜博 1赵衍运1
作者信息
- 1. 北京邮电大学信息与通信工程学院多媒体通信与模式识别实验室 北京 100876
- 折叠
摘要
Abstract
It is well-known that event detection in real-world surveillance videos is a challenging task. The corpus of TRECVID-SED evaluation is acquired from the surveillance video of London Gatwick International Airport and it is well known for its high difficulties. We propose a comprehensive event detection framework based on an effective part-based deep network cascade — head-shoulder networks (HsNet) and tra jectory analysis. On the one hand, the deep network detects pedestrians very precisely, laying a foundation for tracking pedestrians. On the other hand, convolutional neural networks (CNNs) are good at detecting key-pose-based single events. Trajectory analysis is introduced for group events. In TRECVID-SED15 evaluation, our approach outperformed others in 3 out of 6 events, demonstrating the power of our proposal.关键词
卷积神经网络/事件检测/行人检测/目标跟踪/轨迹分析Key words
Convolutional neural network (CNN)/event detection/pedestrian detection/target tracking/trajectory analysis引用本文复制引用
王梦来,李想,陈奇,李澜博,赵衍运..基于CNN的监控视频事件检测[J].自动化学报,2016,42(6):892-903,12.
