文献：DaSiamRPN: Zheng Zhu, Qiang Wang, Bo Li, Wu Wei, Junjie Yan, Weiming Hu."Distractor-aware Siamese Networks for Visual Object Tracking." ECCV (2018). [paper][github]
文章主要贡献
1.训练数据的扩充
- 加入Detection pair (ImageNet,COCO中做数据增广)
- negative simple in same categories (Called Distractor-aware Training)
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negative simple in different categories (Called Distractor-aware Training)
2.Distractor Model
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引入Distractor Model,将Proposal与exemplar的相似性度量得分减去所有之前预先得到的Distrator(NMS将网络提出的proposal去冗余,去掉classification score最高的proposal，在剩下的Distrator set 中保留score大于给定阈值的proposal)与当前proposal 的score(相似性度量)的加权和的平均
3.long term Tracking
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当追丢时用local to global stategy 恒定step迭代的增加搜索区域的大小
具体而言
## 1. 扩充数据集
detection pairs negative pair from same categoriess negative pairs from different categories 2. Distractor Model
TEST:\(\Gamma(n)=(n-1)!\quad\forall n\in\mathbb N\)
传统的SiamTracking是用求相似性度量用以下公式：
\[f(x)=\varphi(x)*\varphi(z)+b\cdot\mathbf{1}\] - 作者提出将NMS将网络提出的proposal去冗余,去掉classification score最高的proposal，在剩下的Distrator set 中保留score大于给定阈值的proposal)与当前proposal 的score(相似性度量)的加权和的平均
\[q=\mathop{\arg\max}\limits_{p_{k}\in\mathcal{P}} f(z,p_{k})- \frac{\hat{\alpha}-\sum_{i=1}^{n}\alpha_{i}f(d_{i},p_{k})} {\sum_{i=1}^{n}\alpha_{i}}\]
\(\mathcal{P}\) 是score在top-k的proposal, \(\alpha_{i}\)是每个干扰proposal的权重(paper中是全为1), \(d_{i}\)是第 \(i\) 个 distractor proposal 因为自相关操作是线性的，则将\(\varphi(p_{k})\)提出来：
\[q=\mathop{\arg\max}\limits_{p_{k}\in\mathcal{P}}(\varphi(z)-\frac{\hat{\alpha}\sum_{i=1}^{n}\alpha_{i}f(d_{i},p_{k})} {\sum_{i=1}^{n}\alpha_{i}})*\varphi(p_{k})\]
3.Long term Tracking
- 当追丢时用local to global stategy 恒定step迭代的增加搜索区域的大小