论文标题
利用视频异常检测的时空相关性
Exploiting Spatial-temporal Correlations for Video Anomaly Detection
论文作者
论文摘要
由于异常事件的歧义和多样性，视频异常检测（VAD）仍然是模式识别界的一项具有挑战性的任务。现有的基于深度学习的VAD方法通常利用代理任务来学习正常模式，并区分偏离异常模式的实例。但是，它们中的大多数并没有充分利用视频帧之间的时空相关性，这对于理解正常模式至关重要。在本文中，我们通过在短期和短期内学习外观和运动的演化规律性，并在正常视频中连续帧之间利用空间 - 周期性相关性来解决无监督的VAD。具体而言，我们提议利用时空长的短期记忆（ST-LSTM）来提取和记住统一记忆细胞中的空间外观和时间变化。此外，受生成对抗网络的启发，我们引入了一个歧视者，以使用ST-LSTM进行对抗性学习以增强学习能力。标准基准的实验结果证明了无监督VAD的时空相关性的有效性。与最先进的方法相比，我们的方法在UCSD PED2，CUHK Avenue和Shanghaitech的AUC中获得了竞争性能。
Video anomaly detection (VAD) remains a challenging task in the pattern recognition community due to the ambiguity and diversity of abnormal events. Existing deep learning-based VAD methods usually leverage proxy tasks to learn the normal patterns and discriminate the instances that deviate from such patterns as abnormal. However, most of them do not take full advantage of spatial-temporal correlations among video frames, which is critical for understanding normal patterns. In this paper, we address unsupervised VAD by learning the evolution regularity of appearance and motion in the long and short-term and exploit the spatial-temporal correlations among consecutive frames in normal videos more adequately. Specifically, we proposed to utilize the spatiotemporal long short-term memory (ST-LSTM) to extract and memorize spatial appearances and temporal variations in a unified memory cell. In addition, inspired by the generative adversarial network, we introduce a discriminator to perform adversarial learning with the ST-LSTM to enhance the learning capability. Experimental results on standard benchmarks demonstrate the effectiveness of spatial-temporal correlations for unsupervised VAD. Our method achieves competitive performance compared to the state-of-the-art methods with AUCs of 96.7%, 87.8%, and 73.1% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech, respectively.
