Efficient hybrid appearance model for object tracking with occlusion handling

Abstract

During object tracking, the appearance of the object being tracked often suffers from complex variations due to large illumination, pose changes, and frequent occlusions. Modeling a robust appearance model for accommodating those variations becomes a crux for appearance-based tracking algorithms. We present a tracking algorithm based on the hybrid appearance models that include a fixed appearance model, a fast-change appearance model, and an eigenspace-based model. The hybrid appearance models reveal three factors of appearance changes: stable, transient, and gradual. A particle filter is invoked to perform state inference and simultaneously be responsible for intermodel switching. In addition, robust statistics is utilized to deal with occlusion events. Numerous experiments on many difficult sequences demonstrate that our algorithm can effectively track the object undergoing large pose, lighting, and viewpoint changes. More importantly, our algorithm shows powerful ability of addressing severe occlusions.