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.

视频异常检测中，除了视觉信号外，我们还应用了骨架序列。我们提出了一种完整的骨架轨迹表示方法，以学习不同时间段内预期的动作。我们使用多任务学习来重建轨迹的任何未观测连续时间段，从而实现过去或未来时间段的外推和插值。我们采用了端到端的基于注意力的编码器 - 解码器模型，并对轨迹进行编码，联合学习被时间遮挡的轨迹段的潜在表示，并根据不同时间段内的预期动作进行重建。在三个基于轨迹的视频异常检测数据集上进行的大量实验证明了我们方法的优势和有效性，并在骨架轨迹的异常检测中取得了最先进的结果。