Discriminative tracking using tensor pooling

Bo Ma, Lianghua Huang, Jianbing Shen, Ling Shao

Research output: Contribution to journalArticle

51 Citations (Scopus)
4 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)2411-2422
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume46
Issue number11
Early online date28 Sep 2015
DOIs
Publication statusPublished - 1 Nov 2016

Cite this