A Novel Approach Automatic Detection of Suspicious Behavior

Abstract:

We propose an efficient method for automatic detection of suspicious behavior in video surveillance data. First of all, we cluster a set of sequences labeled as normal or suspicious. Then, we assign new observation sequences to behavior clusters. We label a sequence as suspicious if it maps to an existing model of suspicious behavior or does not map to any existing model according to the corresponding HMMs. We evaluate our proposed method on a real-world video surveillance and find that the method is very effective at detecting suspicious behavior.

• Periodical: Advanced Materials Research (Volumes 962-965)
• Pages: 2838-2841
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.962-965.2838
• Online since: June 2014
• Authors: Shao Ping Zhu, Yu Hua Chen
• Keywords: Automatic Detection, Observation Sequences, Suspicious Behavior, Video Surveillance

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