Video Object Extraction Using Model Matching Technique: A Novel Approach

Emerging video standards addresses mainly content-based functionalities and interactive multimedia. This requires a prior decomposition of sequences into semantically meaningful physical objects. We formulate this problem as one of separating foreground objects from the background based on motion information. The algorithm begins with the motion estimation accomplished using ARPS (adaptive Rood pattern search) algorithm on any two frames of image sequence. A binary object model representing edge features is derived using Canny edge detector and tracked in subsequent frames using motion vectors generated from ARPS algorithm. The object model is updated for each frame to accommodate for complex motions and shape changes. Motion based region merging is performed finally to merge regions with similar motion to extract moving objects. Experimental results demonstrate the performance of our proposed algorithm.