Abnormal Behavior Detection Algorithm With Video-bag Attention Mechanism in Surveillance Video
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摘要: 针对监控视频中行人非正常行走状态的异常现象, 提出了一个端到端的异常行为检测网络, 以视频包为输入, 输出异常得分. 时空编码器提取视频包时空特征后, 利用基于隐向量的注意力机制对包级特征进行加权处理, 最后用包级池化映射出视频包得分. 本文整合了4个常用的异常行为检测数据集, 在整合数据集上进行算法测试并与其他异常检测算法进行对比. 多项客观指标结果显示, 本文算法在异常事件检测方面有着显著的优势.Abstract: Aiming at the detection of the abnormal behavior pedestrians in surveillance videos, this paper proposes an end-to-end abnormal behavior detection network. It takes video bags as input, and anomaly score as output. The spatio-temporal encoder is used to extract the features of the video bag, then use the attention mechanism based on the hidden vector to weight the different elements in the bag-level feature, and finally use the bag-level pooling to obtain the video bag anomaly score. Four commonly used anomaly detection datasets are integrated and used to test and compare the performance of different anomaly detection algorithm. The results of multiple objective indicators show that our algorithm has significant advantages in anomaly detection.
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Key words:
- Anomaly detection /
- video-bag /
- spatiotemporal feature /
- attention mechanism
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图 7 异常检测算法在帧级及事件级指标对比图
Fig. 7 The frame-level and event-level index of different algorithms
表 1 异常检测算法AUC及EER指标
Table 1 The AUC and EER of different algorithms
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表 2 算法处理时间(CPU) (ms)
Table 2 The processing time of algorithms (CPU) (ms)
时空自编码器 变分编码器 本文算法 238 245 173
下载: 导出CSV
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