论文标题
基于多任务学习的视频异常检测
Multi-Task Learning based Video Anomaly Detection with Attention
论文作者
论文摘要
基于多任务学习的视频异常检测方法结合了不同分支机构中的多个代理任务，以在不同情况下检测视频异常。大多数现有方法要么不将互补任务结合起来以有效涵盖所有运动模式，要么没有明确考虑对象的类别。为了解决上述缺点，我们提出了一种基于多任务学习的新型方法，该方法结合了互补的代理任务，以更好地考虑运动和外观特征。我们将语义分割和未来的框架预测任务结合在一个分支中，以学习对象类和一致的运动模式，并同时检测相应的异常。在第二个分支中，我们添加了几种注意机制，以检测运动异常，并注意对象部分，运动方向以及对象与摄像机的距离。我们的定性结果表明，提出的方法有效地考虑了对象类，并以对上述重要因素的关注学习运动，从而导致精确运动建模和更好的运动异常检测。此外，定量结果表明，与最先进的方法相比，我们方法的优越性。
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.
