Abnormal event detection is an important task in video surveillance systems. In this paper, we propose a novel bidirectional multi-scale aggregation networks (BMAN) for abnormal event detection. The proposed BMAN learns spatiotemporal patterns of normal events to detect deviations from the learned normal patterns as abnormalities. The BMAN consists of two main parts: an inter-frame predictor and an appearancemotion joint detector. The inter-frame predictor is devised to encode normal patterns, which generates an inter-frame using bidirectional multi-scale aggregation based on attention. With the feature aggregation, robustness for object scale variations and complex motions is achieved in normal pattern encoding. Based on the encoded normal patterns, abnormal events are detected by the appearance-motion joint detector in which both appearance and motion characteristics of scenes are considered. Comprehensive experiments are performed, and the results show that the proposed method outperforms the existing state-of-the-art methods. The resulting abnormal event detection is interpretable on the visual basis of where the detected events occur. Further, we validate the effectiveness of the proposed network designs by conducting ablation study and feature visualization.

中文翻译：

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BMAN：用于异常事件检测的双向多尺度聚合网络。


异常事件检测是视频监控系统中的一项重要任务。在本文中，我们提出了一种用于异常事件检测的新型双向多尺度聚合网络（BMAN）。所提出的 BMAN 学习正常事件的时空模式，以检测与学习的正常模式的偏差为异常。 BMAN 由两个主要部分组成：帧间预测器和外观运动联合检测器。帧间预测器被设计为对正常模式进行编码，其使用基于注意力的双向多尺度聚合来生成帧间。通过特征聚合，在正常模式编码中实现了对对象尺度变化和复杂运动的鲁棒性。基于编码的正常模式，通过外观-运动联合检测器检测异常事件，其中同时考虑场景的外观和运动特征。进行了全面的实验，结果表明所提出的方法优于现有的最先进的方法。由此产生的异常事件检测可以根据检测到的事件发生的位置进行视觉解释。此外，我们通过进行消融研究和特征可视化来验证所提出的网络设计的有效性。