Localization guided fight action detection in surveillance videos

Automatic detection of fight behaviors in surveillance videos is an important task for surveillance systems. In this work, we propose a novel localization guided framework for detecting fight actions in surveillance videos. Specifically, we exploit optical flow maps to extract motion activation information, which indicates the location of active regions. Then, a detection guided alignment module is designed to adjust the localized active regions. This approach employs a two-stream based 3D convolution network as the backbone network with a novel motion acceleration representation on the temporal stream. While most existing methods are still evaluated on three benchmark datasets which were not originally collected from surveillance scenarios, we present a novel Fight Action Detection in Surveillance-videos (FADS) dataset for this purpose. With a total of 1,520 video clips, the FADS is the largest known dataset in terms of number of surveillance videos with fight scenes. Experimental results on both the benchmark datasets and the FADS show that our proposed localization guided method outperforms state-of-the-art techniques.