A Machine Learning Approach for Localization of Suspicious Objects using Multiple Cameras

Surveillance automation of public places assumes an important role in proactively detection of possible threat to public and in maintaining law and order. Based on a review of the existing approaches followed in monitoring of crowd behavior and the techniques applied to nab absconding suspects, especially in public places like bus stands, railway stations and airports, the paper proposes surveillance automation i.e. automating the process of detecting, recognizing the suspects and suspicious behavior of the people in the crowd. The process involves not only automatically detecting and recognizing known criminals, but also tracking of movements of persons and objects and notifying the authorities of any suspicious behaviour on the basis of machine learning algorithms. The proposed system, that has automated the surveillance process with multiple cameras was found to be working in simulated environment, can prevent unfortunate incidents in public places.