Automatic fight detection in surveillance videos. Issue 2 (5th June 2017)

• Record Type: Journal Article
• Authors: Fu, Eugene Yujun; Leong, Hong Va; Ngai, Grace; Chan, Stephen C.F.
• Is Part Of: International journal of pervasive computing and communications. Volume 13:Issue 2(2017)
• Page Start: 130
• Page End: 156
• Publication Date: 2017-06-05
• Subjects: Optical flow -- Bag-of-words -- MoSIFT -- Motion analysis -- Real and simulated fights; Ubiquitous computing -- Periodicals; Mobile computing -- Periodicals; Computer network protocols -- Periodicals; Computer network architectures -- Periodicals; Application software -- Development -- Periodicals; 004.6
• DOI: 10.1108/IJPCC-02-2017-0018
• Languages: English
• ISSNs: 1742-7371

Abstract

Abstract : Purpose: Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner. Design/methodology/approach: Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words. Findings: The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach. Originality/value: By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.