CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Fast-deepKCF Without Boundary Effect
Linyu Zheng1,2; Ming Tang1,2; Yingying Chen1,2; Jinqiao Wang1,2; Hanqing Lu1,2
2019-11
Conference NameIEEE International Conference on Computer Vision
Conference Date4020-4029
Conference PlaceSeoul Korea
Abstract

In recent years, correlation filter based trackers (CF trackers) have received much attention because of their top performance. Most CF trackers, however, suffer from low frame-per-second (fps) in pursuit of higher localization accuracy by relaxing the boundary effect or exploiting the high-dimensional deep features. In order to achieve real-time tracking speed while maintaining high localization accuracy, in this paper, we propose a novel CF tracker, fdKCF*, which casts aside the popular acceleration tool, i.e., fast Fourier transform, employed by all existing CF trackers, and exploits the inherent high-overlap among real (i.e., noncyclic) and dense samples to efficiently construct the kernel matrix. Our fdKCF* enjoys the following three advantages. (i) It is efficiently trained in kernel space and spatial domain without the boundary effect. (ii) Its fps is almost independent of the number of feature channels. Therefore, it is almost real-time, i.e., 24 fps on OTB-2015, even though the high-dimensional deep features are employed. (iii) Its localization accuracy is state-of-the-art. Extensive experiments on four public benchmarks, OTB-2013, OTB-2015, VOT2016, and VOT2017, show that the proposed fdKCF* achieves the state-of-the-art localization performance with remarkably faster speed than C-COT and ECO.

Indexed ByEI
Funding ProjectNational 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目标检测、跟踪与识别
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44851
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. Fast-deepKCF Without Boundary Effect[C],2019.
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