Object Matching using Skeletonization based on Hamming Distance

of an object from a dynamic background is a challenging process in computer vision and pattern matching research. The proposed algorithm identifies moving objects from the sequence of video frames which contains dynamically changing backgrounds in the noisy environment. In connection with our previous work, here we have proposed a methodology to perform skeletanization of an object and identifies it. The recent vehicle recognition methods could fail to recognize an object and produce more false acceptance rate(FAR) or false rejection rate (FRR). This paper recommends a method for object identification using weighted distance to extract features. Experimental results and comparisons using real data demonstrate the pre-eminence of the proposed approach.