This study proposes a method to detect idle object and applies it for analysis of suspicious events. Partitioning and Normalized Cross Correlation (PNCC) based algorithm is proposed for the detection of moving object. This algorithm takes less processing time, which increases the speed and also the detection rate. In this an approach is proposed for the detection and tracking of moving object in an image sequence. Two consecutive frames from image sequence are partitioned into four quadrants and then the Normalized Cross Correlation (NCC) is applied to each sub frame. The sub frame which has minimum value of NCC, indicates the presence of moving object. The proposed system is going to use the suspicious tracking of human behaviour in video surveillance and it is mainly used for security purpose in ATM application. The suspicious object's visual properties so that it can be accurately segmented from videos. After analyzing its subsequent motion features, different abnormal events like robbery can be effectively detected from videos. The suspicious action in ATM are many, such as using mobile phones, multiple persons trying to access the ATM machine in same time, kicking of each other, idle object and it shows event corresponding to Vandalism and robbery. In proposed system, idle object detection is used to identify by using PNCC algorithm with P-filter (Particle) and by extracting the features of the object in an enhanced way by using the curvelet based transformation.
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