Robust Recognition of Specific Human Behaviors in Crowded Surveillance Video Sequences. (26th April 2010)

• Record Type: Journal Article
• Authors: Takahashi Takahashi, Masaki Masaki; Fujii Fujii, Mahito Mahito; Shibata Shibata, Masahiro Masahiro; Satoh Satoh, Shin'ichi Shin'ichi
• Other Names: Kim Kim ChangIck ChangIck Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-04-26
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/801252
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : We describe a method that can detect specific human behaviors even in crowded surveillance video scenes. Our developed system recognizes specific behaviors based on the trajectories created by detecting and tracking people in a video. It detects people using an HOG descriptor and SVM classifier, and it tracks the regions by calculating the two-dimensional color histograms. Our system identifies several specific human behaviors, such as running and meeting, by analyzing the similarities to the reference trajectory of each behavior. Verification techniques such as backward tracking and calculating optical flows contributed to robust recognition. Comparative experiments showed that our system could track people more robustly than a baseline tracking algorithm even in crowded scenes. Our system precisely identified specific behaviors and achieved first place for detecting running people in the TRECVID 2009 Surveillance Event Detection Task.