Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection
This addresses the problem of detecting abnormal events in crowded scenes for surveillance applications, offering an incremental improvement by reducing the need for hand-crafted features and fine-tuning.
The paper tackles crowd abnormal event detection by proposing a plug-and-play CNN method that combines semantic CNN features with optical flow to measure local abnormalities without fine-tuning, achieving superior results on challenging datasets compared to state-of-the-art methods.
Most of the crowd abnormal event detection methods rely on complex hand-crafted features to represent the crowd motion and appearance. Convolutional Neural Networks (CNN) have shown to be a powerful tool with excellent representational capacities, which can leverage the need for hand-crafted features. In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality. We specifically propose a novel measure-based method which allows measuring the local abnormality in a video by combining semantic information (inherited from existing CNN models) with low-level Optical-Flow. One of the advantage of this method is that it can be used without the fine-tuning costs. The proposed method is validated on challenging abnormality detection datasets and the results show the superiority of our method compared to the state-of-the-art methods.