论文标题
查看相邻的帧：视频异常检测而无需离线培训
Look at Adjacent Frames: Video Anomaly Detection without Offline Training
论文作者
论文摘要
我们提出了一个解决方案，以检测视频中的异常事件，而无需离线训练模型。具体而言，我们的解决方案基于一个随机定量的多层感知器，该多层概念可以在线优化，从其频率信息中重建视频帧，像素像素。基于相邻帧之间的信息变化，在观察每个帧之后，使用增量学习者来更新多层感知器的参数，从而允许沿视频流检测异常事件。不需要离线培训的传统解决方案仅限于只有几个异常帧的视频操作。我们的解决方案打破了这一限制，并在基准数据集上实现了强劲的性能。
We propose a solution to detect anomalous events in videos without the need to train a model offline. Specifically, our solution is based on a randomly-initialized multilayer perceptron that is optimized online to reconstruct video frames, pixel-by-pixel, from their frequency information. Based on the information shifts between adjacent frames, an incremental learner is used to update parameters of the multilayer perceptron after observing each frame, thus allowing to detect anomalous events along the video stream. Traditional solutions that require no offline training are limited to operating on videos with only a few abnormal frames. Our solution breaks this limit and achieves strong performance on benchmark datasets.
