title: Recognition of aggressive human behavior using binary local motion descriptors.
abstract: Video surveillance is an alternative approach to staff or self-reporting that has the potential to detect and monitor aggressive behaviors more accurately. In this paper, we propose an automatic algorithm capable of recognizing aggressive behaviors from video records using local binary motion descriptors. The proposed algorithm may increase the accuracy for retrieving aggressive behaviors from video records, and thereby facilitates scientific inquiry into this low frequency but high impact phenomenon that eludes other measurement approaches.
authors.affiliation: School of Computer Science, Carnegie Mellon University, USA.
authors.forename: Datong
authors.initials: D
authors.lastname: Chen
authors.forename: Howard
authors.initials: H
authors.lastname: Wactlar
authors.forename: Ming-Yu
authors.initials: MY
authors.lastname: Chen
authors.forename: Can
authors.initials: C
authors.lastname: Gao
authors.forename: Ashok
authors.initials: A
authors.lastname: Bharucha
authors.forename: Alex
authors.initials: A
authors.lastname: Hauptmann
mesh_terms: D000374:Aggression / Q000502:physiology*; D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*; D051598:Whole Body Imaging / Q000379:methods*