Unusual Event Detection in Low Resolution Video for enhancing ATM security
Abstract— In real world applications, tracking target in low resolution video is a challenging task because there is loss of discriminative detail in the visual appearance of moving object. The existing methods are mostly based on the enhancement of LR (low resolution) video by super resolution techniques. But these methods require high computational cost. This cost further increases if we are dealing with events detection. We present an algorithm which is able to detect unusual events without such type of conversion and well suited for enhancement of security of ATMs where conventional low resolution cameras are generally used due to their low cost. Proposed algorithm only uses close morphological operation with disk like structuring element in the preprocessing steps to cope up with low resolution video. It further uses rolling average background subtraction technique to detect foreground object from dynamic background in a scene. Our proposed algorithm is able to recognize the occurrence of uncommon events < Final Year Projects 2016 > such as overcrowding or fight in the low resolution video simply by using statistical property, standard deviation of moving objects. It is fast enough because it process low resolution frames and could be helpful in surveillance system for enhancing the security of ATMs where conventional camera of low resolution are still used. It does not use any classifier and avoids the requirement of training the system initially.
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