PROBABILISTIC BOUNDARY-BASED CONTOUR TRACKING WITH SNAKES IN NATURAL CLUTTERED VIDEO SEQUENCES

Abstract

Moving object detection and tracking in video sequences is a task that emerges in various research fields of video processing, including video analysis and understanding, object-based coding and many related applications, such as content-based retrieval, remote surveillance and object recognition. This work revisits one of the most popular deformable templates for shape modeling and object tracking, the Snakes, and proposes a modified snake model and a probabilistic utilization of it for object tracking. Special attention has been drawn to complex natural (indoor and outdoor) sequences, where temporal clutter, abrupt motion and external lighting changes are crucial for the accuracy of the results, also focusing on the ability of the proposed approach to handle specific HCI problems, such as face and facial feature tracking. A variety of image sequences are used to illustrate the method's capability, providing theoretical explanation as well as experimental verification in specific tracking problems.