Visual tracking via structural patch-based dictionary pair learning

In this paper, a novel visual tracking framework based on Structural Patch-based Dictionary Pair Learning (SPDPL), is proposed. The proposed representation model encapsulates partial and spatial structural variations of the target through a novel dictionary learning scheme. The proposed method facilitates learning a robust and discriminative dictionary by considering all patches from the same part of the target region as one class, thus transforming the tracking problem into a multi-class classification and reconstruction task. Finally, a simple yet effective observation model is designed to obtain the most optimal candidate during tracking. Systems experiments of the proposed tracking algorithm on the Object Tracker Benchmark (OTB) dataset have demonstrated improvements against several other state-of-the-art trackers.

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