Video anomaly detection deals with the recognition of abnormal events in videos. Apart from the visual signal, video anomaly detection has also been addressed with the use of skeleton sequences. We propose a holistic representation of skeleton trajectories to learn expected motions across segments at different times. Our approach uses multitask learning to reconstruct any continuous unobserved temporal segment of the trajectory allowing the extrapolation of past or future segments and the interpolation of in-between segments. We use an end-to-end attention-based encoder-decoder. We encode temporally occluded trajectories, jointly learn latent representations of the occluded segments, and reconstruct trajectories based on expected motions across different temporal segments. Extensive experiments on three trajectory-based video anomaly detection datasets show the advantages and effectiveness of our approach with state-of-the-art results on anomaly detection in skeleton trajectories.

翻译：视频异常检测旨在识别视频中的异常事件。除视觉信号外，该领域也利用骨架序列进行处理。我们提出了一种骨架轨迹的整体表示方法，用于学习不同时间段内各分段的预期运动。该方法采用多任务学习，可重构轨迹中任意连续未观测时间分段，从而实现对过去或未来分段的推断以及中间分段的内插。我们使用基于注意力机制的端到端编码器-解码器架构：对时间遮挡轨迹进行编码，联合学习遮挡分段的潜在表示，并基于不同时间分段的预期运动重构轨迹。在三个基于轨迹的视频异常检测数据集上进行的广泛实验表明，我们的方法在骨架轨迹异常检测中取得了最先进的结果，展现了其优势与有效性。