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An Intelligent Suspicious Activity Detection Framework (ISADF) for Video Surveillance Systems

Video Surveillance systems are playing vital role in ensuring the security at various public places like bus stops, railway stations, shopping malls, Airports etc. Suspicious activity recognition helps to prevent from threats and identify the causes after threat. Existing semi-automatic approaches depends on human intervention to detect the uncommon activities and suspicious behavior from video context. Due to these limitations they become non-intelligence, very slow and need more human observers. In this paper, to overcome these problems an Intelligent Suspicious Activity Detection Framework (ISADF) for Video data is proposed. This framework uses location dependent training data for intelligence and context (foreground) change information for suspicious activity detection. Experimental results show that ISADF is a high speed intelligent threat detection system than existing approaches.