Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model

Understand

Fight detection in videos is an emerging deep learning application with today's prevalence of surveillance systems and streaming media.

• Previous work has largely relied on action recognition techniques to tackle this problem.
• In this paper, we propose a simple but effective method that solves the task from a new perspective: we design the fight detection model as a composition of an action-aware feature extractor and an anomaly score generator.
• Also, considering that collecting frame-level labels for videos is too laborious, we design a weakly supervised two-stage training scheme, where we utilize multiple-instance-learning loss calculated on video-level labels to train the score generator, and adopt the self-training technique to further improve its performance.