Abstract
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.
Abstract (translated)
在基于姿势的视频异常检测中，现有技术通常假设异常事件主要源于不常见的行为。然而，与使用人体骨架表示不同的是，我们探索了利用2D轮廓学习正常人类行为的循环运动模式的可能性。尽管保留了基于姿势方法的所有优点（如增强的对象匿名化），从人体骨骼到轮廓的转变被认为为未来研究开放了涵盖更多对象类别的可能性。我们建议将该问题表述为回归和分类任务，并且还探索了两种不同的数据表示技术用于轮廓。 为了进一步减少基于姿势视频异常检测解决方案的计算复杂度，本研究中的所有方法均基于深度学习领域中的浅层神经网络，并在三个最突出的视频异常检测基准数据集及其与人类相关的对应数据集上进行了评估，总计六个数据集。我们的结果表明，这种对基于姿势的视频异常检测的新视角为未来的研究开辟了有希望的方向。
URL
https://arxiv.org/abs/2503.19588
