Multi-Task Learning based Video Anomaly Detection with Attention
This work addresses video anomaly detection for surveillance or security applications, presenting an incremental improvement over existing multi-task learning methods.
The paper tackles video anomaly detection by proposing a multi-task learning method that combines semantic segmentation and future frame prediction in one branch, and adds attention mechanisms in another branch to consider object class and motion patterns. The results show precise motion modeling and superiority over state-of-the-art methods in quantitative evaluations.
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.