【摘要】 复杂监控视频中事件检测是一个具有挑战性的难题,而TRECVID-SED评测使用的数据集取自机场的实际监控视频,以高难度著称.针对TRECVID-SED评测集,提出了一种基于卷积神经网络(Convolutional neural network,CNN)级联网络和轨迹分析的监控视频事件检测综合方案.在该方案中,引入级联CNN网络在拥挤场景中准确地检测行人,为跟踪行人奠定了基础;采用CNN网络检测具有关键姿态的个体事件,引入轨迹分析方法检测群体事件.该方案在国际评测中取得了很好的评测排名:在6个事件检测的评测中,3个事件检测排名第一.更多还原

【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(Hs Net) and trajectory 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.更多还原