A region-based object tracking scheme using Adaboost-based feature selection

This paper presents an object extraction system for video surveillance applications that require pixel-wise extraction accuracy. The proposed mechanism is composed of two trackers. The first tracker extract video objects by using Adaboost on pixel-based global seed features, it can provide more detailed segmentation of target. The second tracker applies bidirectional labeling on regions as well as uses Adaboost on region-based local seed features to refine the object masks obtained from the first tracker. The system is featured with an interactive tool which allows users to deal with serious object occlusion situations. A confidence measure is proposed to minimize the effort of human interactions.