Object tracking via Dirichlet process-based appearance models
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Object tracking is the process of locating objects of interest in video frames. Challenges still exist in handling appearance changes in object tracking for robotic vision. In this paper, we propose a novel Dirichlet process-based appearance model (DPAM) for tracking. By explicitly introducing a new model variable into the traditional Dirichlet process, we model the negative and positive target instances as the combination of multiple appearance models. Within each model, target instances are dynamically clustered based on their visual similarity. DPAM provides an infinite nonparametric mixture of distributions that can grow automatically with the complexity of the appearance data. In addition, prior off-line training or specifying the number of mixture components (clusters or parameters) is not required. We build a tracking system in which DPAM is applied to cluster negative and positive target samples and detect the new target location. Our experimental results on real-world videos show that our system achieves superior performance when compared with several state-of-the-art trackers.