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.
翻译:我们提出一个在视频中检测异常事件的解决方案,而不需要对模型进行离线培训。 具体地说, 我们的解决方案是基于随机初始化的多层感应器,该感应器在网络上最优化地重建视频框架的频率信息, 即像素比素。 根据相邻框架之间的信息变化, 使用一个递增的学习器在观察每个框架后更新多层感应器的参数, 从而能够检测视频流沿线的异常事件。 不需要离线培训的传统解决方案仅限于在只有少数异常框架的视频上操作。 我们的解决方案打破了这一限制,并在基准数据集上取得了很强的绩效 。