用人体轮廓替代骨骼检测异常，提升泛化能力。
Video Anomaly Detection with Contours -- A Study
- 以2D轮廓代替骨骼，学习正常行为的循环运动模式
- 在六个数据集上验证，轮廓方法表现优于传统骨骼法
- 采用浅层神经网络，降低计算开销，适合实时应用
基于姿态的视频异常检测通常假设异常事件源于不常见的行为。与使用人体骨骼表示不同，本文探索利用2D轮廓学习正常人类行为的循环运动模式。在保持姿态方法优势（如对象匿名化）的同时，将表示从骨骼转向轮廓，为未来扩展至更多物体类别提供可能。我们提出将问题建模为回归与分类任务，并研究两种不同的轮廓数据表示方法。为降低现有姿态法的计算复杂度，所有方法均基于深度学习中的浅层神经网络，在视频异常检测领域三个最突出的基准数据集及其对应的人类相关数据集上进行评估，共涉及六个数据集。结果表明，这一新视角为未来研究提供了有前景的方向。
原文摘要 · Abstract (English)
In Pose-based Video Anomaly Detection prior art is rooted on the assumption that abnormal events can be mostly regarded as a result of uncommon human behavior. Opposed to utilizing skeleton representations of humans, however, we investigate the potential of learning recurrent motion patterns of normal human behavior using 2D contours. Keeping all advantages of pose-based methods, such as increased object anonymization, the shift from human skeletons to contours is hypothesized to leave the opportunity to cover more object categories open for future research. We propose formulating the problem as a regression and a classification task, and additionally explore two distinct data representation techniques for contours. To further reduce the computational complexity of Pose-based Video Anomaly Detection solutions, all methods in this study are based on shallow Neural Networks from the field of Deep Learning, and evaluated on the three most prominent benchmark datasets within Video Anomaly Detection and their human-related counterparts, totaling six datasets. Our results indicate that this novel perspective on Pose-based Video Anomaly Detection marks a promising direction for future research.
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