Svrat: A Skeleton-Based Intelligent Monitoring System for Violence Recognition and Abuser Tracking

Recently, intelligent monitoring technologies have been developing rapidly, among them, human action recognition and target tracking have made breakthroughs in their respective fields. However, the huge computational cost makes the integration of the two technologies difficult. In this paper, we design a skeleton-based monitoring system that realizes violent action recognition and abuser tracking with a relatively low overall complexity. For violence recognition, we put forward a novel Skeletal Context Attention Network (SCAN), which is lightweight yet effective to exploit spatial and temporal representations of skeleton data. For abuser tracking, we present a Skeleton-Guided Correlation Filter (SGCF) that can track a perpetrator continuously even in some extreme cases, such as drastic changes in speed or color. Experiments on two benchmark datasets show that the proposed system not only outperforms the existing state-of-the-art methods for violent action recognition and abuser tracking, but also implements the both tasks with less computation.