Proposed system presents an automatic long term tracking and learning and detection of real time objects in the live video stream. In this system, Object to be tracked also called as cropped image is defined by its location and the extent in the single frame by selecting the object of interest in the live video. Many existing systems for tracking objects fails due to loss of information caused by complex shapes, rapid motion, illumination changes, scaling and projection of 3D world on 2D image. Proposed modified PN learning algorithm which uses background subtraction technique to increase speed of the frame processing for object detection. Proposed Modified PN learning algorithm considers the object to be tracked as P-Type Object and background is divided into the numbers of N-Type objects. Initially input image is matched with the N-Type of objects for rejection and then with P-type for acceptance. Proposed system uses the Template Matching algorithm to match cropped image with region of interest in the current frame to mark the Object Location. If match is found then Principle Component Analysis algorithm is used for detection of the fast moving object which is the advantage over the existing systems. If match does not found then Proposed Modified PN learning processing is applied to detect the image in rapid motion video. Proposed system uses background subtraction to increase the performance for detection of any moving object as the background remains still and we get approximate location of the moving object. Proposed System is expected to minimize delay for frame processing and reduce average localization errors to improve in matching percentage irrespective of scaling of the input image. Thus proposed system is expected to overcome the drawbacks of existing system for efficient tracking of any real time object.

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Template matching Tracking Adaptive Learning Object Detection