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.
翻译:多任务基于学习的视频异常现象探测方法将不同分支的多重代理任务结合起来,以发现不同情况下的视频异常现象。大多数现有方法要么没有将有效覆盖所有运动模式的补充任务结合起来,要么没有明确考虑对象类别。为解决上述缺陷,我们建议采用新的多任务学习方法,将互补的代理任务结合起来,以更好地考虑运动和外观特征。我们将语义分割和未来框架预测任务合并到一个分支,以学习对象类别和一致运动模式,同时检测各自的异常现象。在第二分支,我们增加了几个关注机制,以检测运动异常现象,关注对象部分、运动方向和物体与相机的距离。我们的质量结果显示,拟议方法有效地考虑对象类别,学习运动,同时注意上述重要因素,从而形成精确的运动模型和更好的运动异常探测。此外,定量结果显示我们的方法优于最先进的方法。