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Robust visual tracking by embedding combination and weighted-gradient optimization
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DOI：10.1016/j.patcog.2020.107339.png)
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Abstract
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Existing tracking-by-detection approaches build trackers on binary classifiers. Despite achieving state-of-the-art performance on tracking benchmarks, these trackers pay limited attention to data imbalance issue, e.g, positive and negative, easy and hard. In this paper, we demonstrate that separately learning feature embeddings corresponding to negative samples with different semantic characteristics is effective in reducing the background diversity to handle the imbalance between positive and negative samples, which facilitates background awareness of classifiers. Specifically, we propose a negative sample embedding combination network, which helps to learn several sub-embeddings and combine them to build a robust classifier. In addition, we propose a weighted-gradient loss to handle the imbalance between easy and hard samples. The gradient contribution of each sample to model training is dynamically weighted according to the gradient distribution, which prevents easy samples from overwhelming model training. Extensive experiments on benchmarks demonstrate that our tracker performs favorably against state-of-the-art algorithms. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Visual tracking
Data imbalance
Embedding combination
Weighted-gradient loss
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