CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
P2T: Part-to-Target Tracking via Deep Regression Learning
Gao, Junyu1,2; Zhang, Tianzhu1,2; Yang, Xiaoshan1,2; Xu, Changsheng1,2
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2018-06
Volume27Issue:6Pages:3074-3086
SubtypeArticle
AbstractMost existing part-based tracking methods are part-to-part trackers, which usually have two separated steps including the part matching and target localization. Different from existing methods, in this paper, we propose a novel part-to-target (P2T) tracker in a unified fashion by inferring target location from parts directly. To achieve this goal, we propose a novel deep regression model for P2T regression in an end-to-end framework via convolutional neural networks. The proposed model is designed not only to exploit the part context information to preserve object spatial layout structure, but also to learn part reliability to emphasize part importance for the robust P2T regression. We evaluate the proposed tracker on four challenging benchmark sequences, and extensive experimental results demonstrate that our method performs favorably against state-of-the-art trackers because of the powerful capacity of the proposed deep regression model.
KeywordVisual Tracking Deep Learning Part-based Tracker
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2018.2813166
WOS KeywordROBUST VISUAL TRACKING ; OBJECT TRACKING ; BENCHMARK ; MODEL
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61432019 ; Key Research Program of Frontier Sciences, CAS(QYZDJ-SSW-JSC039) ; Beijing Natural Science Foundation(4172062) ; 61572498 ; 61532009 ; 61702511 ; 61572296)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000428930600014
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21999
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Gao, Junyu,Zhang, Tianzhu,Yang, Xiaoshan,et al. P2T: Part-to-Target Tracking via Deep Regression Learning[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2018,27(6):3074-3086.
APA Gao, Junyu,Zhang, Tianzhu,Yang, Xiaoshan,&Xu, Changsheng.(2018).P2T: Part-to-Target Tracking via Deep Regression Learning.IEEE TRANSACTIONS ON IMAGE PROCESSING,27(6),3074-3086.
MLA Gao, Junyu,et al."P2T: Part-to-Target Tracking via Deep Regression Learning".IEEE TRANSACTIONS ON IMAGE PROCESSING 27.6(2018):3074-3086.
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