CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
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
Conference NameEuropean Conference on Computer Vision
Pages759–775
Conference Date2020-8
Conference PlaceOnline
Abstract

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.

Indexed ByEI
Funding ProjectNational Nature Science Foundation of China[61876086] ; National Natural Science Foundation of China[61702510] ; National Natural Science Foundation of China[61806200] ; National Natural Science Foundation of China[61772527]
Language英语
Sub direction classification图像视频处理与分析
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44855
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorLinyu Zheng
Affiliation1.NLPR
2.CASIA
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Linyu Zheng,Ming Tang,Yingying Chen,et al. Learning Feature Embeddings for Discriminant Model based Tracking[C],2020:759–775.
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