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TRIC-track: tracking by regression with incrementally learned cascades

Wang, Xiaomeng; Valstar, Michel F.; Martinez, Brais; Khan, Muhammad Haris; Pridmore, Tony

Authors

Xiaomeng Wang

Brais Martinez

Muhammad Haris Khan

TONY PRIDMORE tony.pridmore@nottingham.ac.uk
Professor of Computer Science



Abstract

This paper proposes a novel approach to part-based track- ing by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations. We propose to use cascaded regression with incremental learning to track generic objects without any prior knowledge of an object’s structure or appearance. We exploit the spatial constraints between parts by implicitly learning the shape and deformation parameters of the object in an online fashion. We integrate a multiple temporal scale motion model to initialise our cascaded regression search close to the target and to allow it to cope with occlusions. Experimental results show that our tracker ranks first on the CVPR 2013 Benchmark.

Citation

Wang, X., Valstar, M. F., Martinez, B., Khan, M. H., & Pridmore, T. (2015). TRIC-track: tracking by regression with incrementally learned cascades. In 2015 IEEE International Conference on Computer Vision (ICCV), 4337-4345. doi:10.1109/ICCV.2015.493

Conference Name 2015 IEEE International Conference on Computer Vision (ICCV)
Start Date Dec 7, 2015
End Date Dec 13, 2015
Acceptance Date Sep 15, 2015
Online Publication Date Dec 13, 2015
Publication Date Dec 13, 2015
Deposit Date Jan 21, 2016
Publicly Available Date Jan 21, 2016
Peer Reviewed Peer Reviewed
Pages 4337-4345
Book Title 2015 IEEE International Conference on Computer Vision (ICCV)
ISBN 978-1-4673-8390-5
DOI https://doi.org/10.1109/ICCV.2015.493
Public URL http://eprints.nottingham.ac.uk/id/eprint/31307
Publisher URL https://ieeexplore.ieee.org/document/7410850
Related Public URLs http://pamitc.org/iccv15/program.php
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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