Multi-task learning based video anomaly detection methods combine multiple
proxy tasks in different branches to detect video anomalies in different
situations. Most existing methods either do not combine complementary tasks to
effectively cover all motion patterns, or the class of the objects is not
explicitly considered. To address the aforementioned shortcomings, we propose a
novel multi-task learning based method that combines complementary proxy tasks
to better consider the motion and appearance features. We combine the semantic
segmentation and future frame prediction tasks in a single branch to learn the
object class and consistent motion patterns, and to detect respective anomalies
simultaneously. In the second branch, we added several attention mechanisms to
detect motion anomalies with attention to object parts, the direction of
motion, and the distance of the objects from the camera. Our qualitative
results show that the proposed method considers the object class effectively
and learns motion with attention to the aforementioned important factors which
results in a precise motion modeling and a better motion anomaly detection.
Additionally, quantitative results show the superiority of our method compared
with state-of-the-art methods.

通过多任务学习方法结合异构特征检测，我们提出了一种视频异常检测方法，综合考虑了运动和外观特征，其中包括语义分割和未来帧预测任务来学习目标类和一致的运动模式，同时添加了多种注意机制来检测运动反常，并优于现有方法。