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- W2554112250 abstract "Matching visual appearances of the target object over consecutive frames is a critical step in visual tracking. The accuracy performance of a practical tracking system highly depends on the similarity metric used for visual matching. Recent attempts to integrate discriminative metric learned by sequential visual data (instead of a predefined metric) in visual tracking have demonstrated more robust and accurate results. However, a global similarity metric is often suboptimal for visual matching when the target object experiences large appearance variation or occlusion. To address this issue, we propose in this paper a spatially weighted similarity fusion (SWSF) method for robust visual tracking. In our SWSF, a part-based model is employed as the object representation, and the local similarity metric and spatially regularized weights are jointly learned in a coherent process, such that the total matching accuracy between visual target and candidates can be effectively enhanced. Empirically, we evaluate our proposed tracker on various challenging sequences against several state-of-the-art methods, and the results demonstrate that our method can achieve competitive or better tracking performance in various challenging tracking scenarios. A spatially weighted similarity fusion scheme for more robust visual trackingLocal similarity metric and its weights are jointly learned in a coherent online process.Evaluations show the robustness to partial occlusion with high matching accuracy." @default.
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- W2554112250 date "2017-04-01" @default.
- W2554112250 modified "2023-09-26" @default.
- W2554112250 title "Learning spatially regularized similarity for robust visual tracking" @default.
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- W2554112250 doi "https://doi.org/10.1016/j.imavis.2016.11.016" @default.
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