Wei Zhong - Robust Object Tracking via Sparsity-based Collaborative Model

Abstract

In this paper we propose a robust object tracking algorithm using a collaborative model.

As the main challenge for object tracking is to account for drastic appearance change, we propose a robust appearance model that exploits both holistic templates and local representations.

We develop a sparsity-based discriminative classifier (SDC) and a sparsity-based generative model (SGM). In the SDC module, we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background. In the SGM module, we propose a novel histogram-based method that takes the spatial information of each patch into consideration with an occlusion handing scheme.

Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively and alleviate the drift problem.

Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.

Robust Object Tracking via Sparsity-based Collaborative Model
Wei Zhong, Huchuan Lu, Ming-Hsuan Yang.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012)，Providence, Rhode Island, [PDF][CODE][DATA].

Results and Datasets

	animal [Download data (zip)] [Precision plot] This sequence is from Kwon et al.
	board [Download data (zip)] [Precision plot] This sequence is from Santner et al.
	car11 [Download data (zip)] [Precision plot] This Sequence is from Ross et al.
	caviar [Download data (zip)] [Precision plot] This sequence is from CAVIAR.
	faceocc2 [Download data (zip)] [Precision plot] This sequence is from Babenko et al.
	girl [Download data (zip)] [Precision plot] This sequence is from Babenko et al.
	jumping [Download data (zip)] [Precision plot] This sequence is from Kalal et al.
	shaking [Download data (zip)] [Precision plot] This sequence is from Kwon et al.
	singer1 [Download data (zip)] [Precision plot] This sequence is from Kwon et al.
	panda [Download data (zip)] [Precision plot] This sequence is from our own
	stone [Download data (zip)] [Precision plot] This sequence is from our own

Abstract

Related Papers

Robust Object Tracking via Sparsity-based Collaborative Model Wei Zhong, Huchuan Lu, Ming-Hsuan Yang. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012)，Providence, Rhode Island, [PDF][CODE][DATA].

Results and Datasets

animal

board

car11

caviar

faceocc2

girl

jumping

shaking

singer1

panda

stone

Robust Object Tracking via Sparsity-based Collaborative Model
Wei Zhong, Huchuan Lu, Ming-Hsuan Yang.
In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012)，Providence, Rhode Island, [PDF][CODE][DATA].