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Structure-Regularized Compressive Tracking with Online Data-Driven Sampling
Guo Q.1,2; Feng W.1,2; Zhou C.1,2; Pun C.-M.3; Wu B.4
2017-08-25
Source PublicationIEEE Transactions on Image Processing
ISSN10577149
Volume26Issue:12Pages:5692-5705
Abstract

Being a powerful appearance model, compressive random projection derives effective Haar-like features from non-rotated 4-D-parameterized rectangles, thus supporting fast and reliable object tracking. In this paper, we show that such successful fast compressive tracking scheme can be further significantly improved by structural regularization and online data-driven sampling. Our major contribution is threefold. First, we find that superpixel-guided compressive projection can generate more discriminative features by sufficiently capturing rich local structural information of images. Second, we propose fast directional integration that enables low-cost extraction of feasible Haar-like features from arbitrarily rotated 5-D-parameterized rectangles to realize more accurate object localization. Third, beyond naive dense uniform sampling, we present two practical online data-driven sampling strategies to produce less yet more effective candidate and training samples for object detection and classifier updating, respectively. Extensive experiments on real-world benchmark data sets validate the superior performance, i.e., much better object localization ability and robustness, of the proposed approach over state-of-the-art trackers.

KeywordFast Directional Integration Object Tracking Online Data-driven Sampling Structural Regularization Superpixel-guided Compressive Projection
DOI10.1109/TIP.2017.2745205
URLView the original
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
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Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorFeng W.
Affiliation1.Tianjin University, School Computer Science & Technology, Tianjin 300350, Peoples Republic of China
2.Tianjin University, State Adm Cultural Heritage, Key Res Ctr Surface Monitoring & Anal Cultural Re, Tianjin 300350, Peoples Republic of China
3.University of Macau, Faculty of Science & Technology, Macau 999078, Peoples Republic of China
4.Tianjin University, School Computer Science & Technology, Tianjin Key Lab Adv Networking TANK, Tianjin 300350, Peoples Republic of China
Recommended Citation
GB/T 7714
Guo Q.,Feng W.,Zhou C.,et al. Structure-Regularized Compressive Tracking with Online Data-Driven Sampling[J]. IEEE Transactions on Image Processing,2017,26(12):5692-5705.
APA Guo Q.,Feng W.,Zhou C.,Pun C.-M.,&Wu B..(2017).Structure-Regularized Compressive Tracking with Online Data-Driven Sampling.IEEE Transactions on Image Processing,26(12),5692-5705.
MLA Guo Q.,et al."Structure-Regularized Compressive Tracking with Online Data-Driven Sampling".IEEE Transactions on Image Processing 26.12(2017):5692-5705.
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