Liu Ting, Zhang Chengqing, Niu Xiaodong, Wang Liming
State Key Lab for Electronic Testing Technology, North University of China, Taiyuan, 030051, China.
College of Mechatronics Engineering, North University of China, Tai Yuan, 030051, China.
PLoS One. 2022 May 26;17(5):e0265564. doi: 10.1371/journal.pone.0265564. eCollection 2022.
The existing anomaly detection methods can be divided into two popular models based on reconstruction or future frame prediction. Due to the strong learning capacity, reconstruction approach can hardly generate significant reconstruction errors for anomalies, whereas future frame prediction approach is sensitive to noise in complicated scenarios. Therefore, a solution has been proposed by balancing the merits and demerits of the two models. However, most methods relied on single-scale information to capture spatial features and lacked temporal continuity between the video frames, affecting anomaly detection accuracy. Thus, we propose a novel method to improve anomaly detection performance. Because of the objects of various scales in each video, we select different receptive fields to extract comprehensive spatial features by the hybrid dilated convolution (HDC) module. Meanwhile, the deeper bidirectional convolutional long short-term memory (DB-ConvLSTM) module can remember the temporal information between the consecutive frames. Experiments prove that our method can detect abnormalities in various video scenes more accurately than the state-of-the-art methods in the anomaly-detection task.
现有的异常检测方法可基于重建或未来帧预测分为两种流行模型。由于强大的学习能力，重建方法很难为异常生成显著的重建误差，而未来帧预测方法在复杂场景中对噪声敏感。因此，通过平衡这两种模型的优缺点提出了一种解决方案。然而，大多数方法依赖单尺度信息来捕捉空间特征，并且视频帧之间缺乏时间连续性，影响了异常检测的准确性。因此，我们提出了一种新颖的方法来提高异常检测性能。由于每个视频中存在各种尺度的对象，我们通过混合扩张卷积（HDC）模块选择不同的感受野来提取综合空间特征。同时，更深层次的双向卷积长短期记忆（DB-ConvLSTM）模块可以记住连续帧之间的时间信息。实验证明，在异常检测任务中，我们的方法比现有最先进的方法能更准确地检测各种视频场景中的异常。
