Detecting object in the dynamic background from the noisy image in visual surveillance

Detecting an object from a dynamic background is a challenging process m 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 background subtraction and modernized from moving vehicles in traffic video sequences that combines statistical assumptions of moving objects using the previous frames in the dynamically varying noisy situation. For that, a binary moving objects hypothesis mask is constructed. Then, Kalman filter is utilized for the amalgamation of current background. Shadow and noise removal algorithms are proposed to operate at the lattice which identifies object-level elements. The results of post-processing can be used to detect object more efficiently. Experimental results and comparisons using real data demonstrate the pre-eminence of the proposed approach.