Adaptive multi-cue tracking by online appearance learning

Abstract

This paper proposes a multi-cue based appearance learning algorithm for object tracking. In each frame, the target object is represented by different cues in the image-as-matrix form. This representation can describe the target from different perspectives and can preserve the spatial correlation information inside the target region. Based on these cues, multiple appearance models are learned online by bilinear subspace analysis to account for the target appearance variations over time. Tracking is formulated within the Bayesian inference framework, in which the observation model is constructed by fusing all the learned appearance models. The combination of online appearance modeling and weight update of each appearance model can adapt our tracking algorithm to both the target and background changes. We test our algorithm on a variety of challenging sequences by tracking car, face, pedestrian, and so on. Experimental results and comparisons to several state-of-the-art methods show improved tracking performance.