RiskWheel: Interactive visual analytics for surveillance event detection

Abstract

Detecting human behaviors in vast amounts of video is a challenging task in a variety of real-world applications. Thus an interactive tool designed to support this task with human in the loop is of significance in various domains including public safety and security. In this paper, we design and develop an interactive visual analytics system, RiskWheel, that enables effective analysis of detection results and utilization of user feedback to improve surveillance event detection. In particular, we propose 1) an interactive approach to visualize data with temporal relations and 2) a novel risk ranking method to differentiate detection results and present more informative ones to the user for better interaction. In our experiments, we demonstrate RiskWheel through a case study on TRECVID Surveillance Event Detection (SED) task [1]. The experimental results quantitatively show that RiskWheel outperforms multiple baselines, demonstrating the power of the risk ranking technique.