Learning Feature Embeddings for Discriminant Model based Tracking | |
Linyu Zheng1,2; Ming Tang1,2; Yingying Chen1,2; Jinqiao Wang1,2; Hanqing Lu1,2 | |
2020-08 | |
会议日期 | 2020-8 |
会议地点 | Online |
页码 | 759–775 |
英文摘要 | After observing that the features used in most online discriminatively trained trackers are not optimal, in this paper, we propose a novel and effective architecture to learn optimal feature embeddings for online discriminative tracking. Our method, called DCFST, integrates the solver of a discriminant model that is differentiable and has a closed-form solution into convolutional neural networks. Then, the resulting network can be trained in an end-to-end way, obtaining optimal feature embeddings for the discriminant model-based tracker. As an instance, we apply the popular ridge regression model in this work to demonstrate the power of DCFST. Extensive experiments on six public benchmarks, OTB2015, NFS, GOT10k, TrackingNet, VOT2018, and VOT2019, show that our approach is efficient and generalizes well to class-agnostic target objects in online tracking, thus achieves state-of-the-art accuracy, while running beyond the real-time speed. Code will be made available. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44855] |
专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
通讯作者 | Linyu Zheng |
作者单位 | 1.CASIA 2.NLPR |
推荐引用方式 GB/T 7714 | Linyu Zheng,Ming Tang,Yingying Chen,et al. Learning Feature Embeddings for Discriminant Model based Tracking[C]. 见:. Online. 2020-8. |
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