Discriminative tracking using tensor pooling

Ma, Bo, Huang, Lianghua, Shen, Jianbing and Shao, Ling (2016) Discriminative tracking using tensor pooling. IEEE Transactions on Cybernetics, 46 (11). pp. 2411-2422. ISSN 2168-2267

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Abstract

How to effectively organize local descriptors to build a global representation has a critical impact on the performance of vision tasks. Recently, local sparse representation has been successfully applied to visual tracking, owing to its discriminative nature and robustness against local noise and partial occlusions. Local sparse codes computed with a template actually form a three-order tensor according to their original layout, although most existing pooling operators convert the codes to a vector by concatenating or computing statistics on them. We argue that, compared to pooling vectors, the tensor form could deliver more intrinsic structural information for the target appearance, and can also avoid high dimensionality learning problems suffered in concatenation-based pooling methods. Therefore, in this paper, we propose to represent target templates and candidates directly with sparse coding tensors, and build the appearance model by incrementally learning on these tensors. We propose a discriminative framework to further improve robustness of our method against drifting and environmental noise. Experiments on a recent comprehensive benchmark indicate that our method performs better than state-of-the-art trackers.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 09 Mar 2017 01:41
Last Modified: 22 Apr 2020 02:33
URI: https://ueaeprints.uea.ac.uk/id/eprint/62914
DOI: 10.1109/TCYB.2015.2477879

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