TRIC-track: tracking by regression with incrementally learned cascades

Wang, Xiaomeng, Valstar, Michel F., Martinez, Brais, Khan, Muhammad Haris and Pridmore, Tony (2015) TRIC-track: tracking by regression with incrementally learned cascades. In: International Conference on Computer Vision (ICCV15), 11-18 December 2015, Santiago, Chile.

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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.

Item Type: Conference or Workshop Item (Paper)
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Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
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Depositing User: Valstar, Michel
Date Deposited: 21 Jan 2016 11:17
Last Modified: 04 May 2020 20:06

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