Human Object Tracking in Nonsubsampled Contourlet Domain

The intelligent systems are becoming more important in life. Moving objects tracking is one of the tasks of intelligent systems. This paper proposes the algorithm to track the object in the street. The proposed method uses the amplitude of zernike moment on nonsubsampled contourlet transform to track object depending on context awareness. The algorithm has also been processed successfully such cases as the new object detection, object detection obscured after they reappeared, detecting and tracking objects which successfully intertwined and then separated again. The proposed method tested on a standard large dataset like PEST dataset, CAVIAR dataset and SUN dataset. The author has compared the results with the other recent methods. Experimental results of the proposed method performed well compared to the other methods. Keywords—object tracking; zernike moment; nonsubsampled contourlet transform; context awareness; extracting features