REAL-TIME OBJECT TRACKING AND LEARNING USING TEMPLATE MATCHING

The system that has been proposed here uses a live video stream to enable tracking, learning and detection of real time objects. The object of interest is selected in a cropping action and then it is subsequently tracked with the help of an indicator in the form of a rectangular bounding box. The process mentioned above is a long term process in the sense that the system is perpetually alert regarding the reappearance of the object after a departure from the frame or regarding the deformities in the physical appearance of the object. The proposed system uses the Template Matching algorithm to match the selected object with the ‘Region of Interest’ in the frame to mark the object’s location. If a match is found then the Principle Component Analysis algorithm is used. PN discrimination algorithm has been proposed in this system which uses background subtraction technique to increase the speed of frame processing for object detection. This reduction in frame processing time and the reduction in average localization errors improve the template matching percentage irrespective of scaling of the input image. Thus the proposed system is expected to overcome the drawbacks of existing system which range from loss of information caused by complex shapes, rapid motion, illumination changes, scaling and projection of 3D world on 2D image, etc.