Abstract 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.

中文翻译：

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通过嵌入组合和加权梯度优化的鲁棒视觉跟踪

摘要 现有的逐检测跟踪方法在二元分类器上构建跟踪器。尽管在跟踪基准上实现了最先进的性能，但这些跟踪器对数据不平衡问题的关注有限，例如正负、简单和困难。在本文中，我们证明了分别学习与具有不同语义特征的负样本对应的特征嵌入可以有效地减少背景多样性以处理正负样本之间的不平衡，从而促进分类器的背景意识。具体来说，我们提出了一个负样本嵌入组合网络，它有助于学习几个子嵌入并将它们组合起来构建一个鲁棒的分类器。此外，我们提出了一种加权梯度损失来处理简单样本和困难样本之间的不平衡。每个样本对模型训练的梯度贡献根据梯度分布动态加权，防止简单样本压倒模型训练。对基准的大量实验表明，我们的跟踪器在对抗最先进的算法时表现出色。