Suspicious behavior detection based on DECOC classifier

Video surveillance systems become quickly an essential criteria and requirement for safety systems. The traditional systems based on manual vision should be replaced by intelligent decision. These systems suffer from not only lack of confidence but also from the worst accuracy. Many intelligent systems are proposed in literature to ensure automation and intelligent decision. Suspicious behavior as a particular case of surveillance, presents a great challenge. Problems faced of researchers are the difficulty of the behavior's detection and the real-time processing. This paper is focused in an abnormal behavior detection. A suspicious behavior recognition is proposed using Data-driven Error Correcting Output Code (DECOC) classifier. The proposed is proved in the experiment section in comparison with the most recent approaches.