Abstract
Time-stamp aware anomaly detection in traffic videos is an essential task for the advancement of the intelligent transportation system. Anomaly detection in videos is a challenging problem due to sparse occurrence of anomalous events, inconsistent behavior of a different type of anomalies and imbalanced available data for normal and abnormal scenarios. In this paper, we present a three-stage pipeline to learn the motion patterns in videos to detect a visual anomaly. First, the background is estimated from recent history frames to identify the motionless objects. This background image is used to localize the normal/abnormal behavior within the frame. Further, we detect an object of interest in the estimated background and categorize it into anomaly based on a time-stamp aware anomaly detection algorithm. We also discuss the challenges faced in improving performance over the unseen test data for traffic anomaly detection. Experiments are conducted over Track 3 of NVIDIA AI city challenge 2019. The results show the effectiveness of the proposed method in detecting time-stamp aware anomalies in traffic/road videos.
Abstract (translated)
交通视频中的时间戳感知异常检测是智能交通系统发展的重要任务。视频中的异常检测是一个具有挑战性的问题，因为异常事件的发生率很低，不同类型异常的行为不一致，正常和异常情况下可用数据不平衡。在本文中，我们提出了一个三阶段的管道学习运动模式的视频检测视觉异常。首先，根据最近的历史帧估计背景，以识别静止的对象。此背景图像用于定位帧内的正常/异常行为。此外，我们在估计的背景中检测出感兴趣的对象，并基于时间戳感知异常检测算法将其分类为异常。我们还讨论了在改善交通异常检测中未公开测试数据的性能方面所面临的挑战。在Nvidia AI City Challenge 2019的3号轨道上进行了实验。结果表明，该方法能有效地检测交通/道路视频中的时间戳感知异常。
URL
https://arxiv.org/abs/1906.04574
