Visual Tracking via Nonnegative Regularization Multiple Locality Coding
Paper i proceeding, 2015

This paper presents a novel object tracking method based on approximated Locality-constrained Linear Coding (LLC). Rather than using a non-negativity constraint on encoding coefficients to guarantee these elements nonnegative, in this paper, the non-negativity constraint is substituted for a conventional ℓ2 norm regularization term in approximated LLC to obtain the similar nonnegative effect. And we provide a detailed and adequate explanation in theoretical analysis to clarify the rationality of this replacement. Instead of specifying fixed K nearest neighbors to construct the local dictionary, a series of different dictionaries with pre-defined numbers of nearest neighbors are selected. Weights of these various dictionaries are also learned from approximated LLC in the similar framework. In order to alleviate tracking drifts, we propose a simple and efficient occlusion detection method. The occlusion detection criterion mainly depends on whether negative templates are selected to represent the severe occluded target. Both qualitative and quantitative evaluations on several challenging sequences show that the proposed tracking algorithm achieves favorable performance compared with other state-of-the-art methods.

non-negativity constraint

approximated LLC

weight learning

visual tracking

ℓ2 norm regularization


Fanghui Liu

Shanghai Jiao Tong University

Tao Zhou

Shanghai Jiao Tong University

Jie Yang

Shanghai Jiao Tong University

Irene Yu-Hua Gu

Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

2015 IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, Chile, Dec 11-18

1550-5499 (ISSN)



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Datorseende och robotik (autonoma system)