We present a local anomaly detection method in videos. As opposed to most existing methods that are computationally expensive and are not very generalizable across different video scenes, we propose an adversarial framework that learns the temporal local appearance variations by predicting the appearance of a normally behaving object in the next frame of a scene by only relying on its current and past appearances. In the presence of an abnormally behaving object, the reconstruction error between the real and the predicted next appearance of that object indicates the likelihood of an anomaly. Our method is competitive with the existing state-of-the-art while being significantly faster for both training and inference and being better at generalizing to unseen video scenes.
翻译:我们在视频中展示了一种局部异常现象探测方法。 与大多数计算成本昂贵且在不同视频场景中不太普遍的现有方法相反,我们提议了一个对抗性框架,通过仅仅依靠当前和以往的外观预测一个正常物体在场景下框中外观的外观,来了解时间上的局部外观变化。 在出现异常的外观时,该物体真实与预测的下一次外观之间的重建错误表明出现异常的可能性。 我们的方法与现有最新工艺相比具有竞争力,同时在培训和推论方面速度要快得多,而且要更好地向看不见的视频场景进行普及。