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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.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 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.
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图 5 ObjectPut和PersonRuns事件关键姿态检测的网络结构
Fig. 5 The architecture of CNN for ObjectPut and PersonRuns key-pose detection
表 1 2015年TRECVID-SED评测结果
Table 1 Evaluation Results of TRECVID-SED 2015
排名 其他团队最好成绩(ADCR) ADCR #Targ #CorDet #FA #Miss Embrace 1 0.8680 0.7909 138 36 90 102 ObjectPut 1 1.0160 1.0120 289 2 33 287 PeopleMeet 4 0.8939 1.0426 256 30 278 226 PeopleSplitUp 2 0.8934 0.9387 152 24 168 128 PersonRuns 2 0.5768 0.9700 50 4 87 46 Pointing 1 1.0140 1.0040 794 16 42 778 
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