An Intend Method for Tracking and Detecting Crime using Clustering Techniques

Clustering, Segmenting and tracking multiple humans is a challenging problem in complex situations in which extended occlusion, shadow and/or reflection exists. This method includes two stages, segmentation (detection) and tracking. Human hypotheses are generated by shape analysis of the foreground blobs using human shape model. The segmented human hypotheses are tracked with a Kalman filter with explicit handling of occlusion. The proposed work is concentrated using DBSCAN algorithm, where after tracking, the individual person is analysed for different crimes like riots, arson etc. The verification is done by walking recognition using an articulated human walking model and Density based algorithm. Experiments show that our approach works robustly in very challenging Sequences. Information stored in database are analyzed using data mining techniques back propagation, data association and DBSCAN.