Simultaneous Event Localization and Recognition in Surveillance Video

The ubiquity of video-based surveillance demands automated approaches to analysis of ever-increasing video footages. Action/Event localization and recognition are two critical capabilities in surveillance video analysis, which have been largely addressed separately in the literature. In this paper, we propose an approach to simultaneously localize and recognize visual events from raw surveillance videos, employing an end-to-end learning strategy. Our approach formulates the task as weakly-supervised sequential semantic segmentation, in which we utilize a specific convolutional RNN to capture not only the appearance and the motion information but also their temporal evolution patterns. We tested our approach on the VIRAT 2.0 dataset. The experimental results, in comparison with relevant existing state-of-the-art, suggest that the proposed approach is promising in delivering a practical solution.