Robust and wast visual tracking with two stage sparse optimization.

Baiyang Liu(1,2), Lin Yang(2), Junzhou Huang(1), P. Meer(3), Leiguang Gong(4), C. Kulikowski(1)

(1)Department of Computer Science, Rutgers University, NJ, USA
(2) Department of Radiology, UMDNJ-Robert Wood Johnson Medical School, NJ, USA
(3) Department of Electrical and Computer Engineering, Rutgers University, NJ, USA
(4) IBM T.J. Watson Research, NY, USA

The sparse representation has been widely used in many areas and utilized for visual tracking. Tracking with sparse representation is formulated as searching for samples with minimal reconstruction errors from learned template subspace. However, the computational cost makes it unsuitable to utilize high dimensional advanced features which are often important for robust tracking under dynamic environment. Based on the observations that a target can be reconstructed from several templates, and only some of the features with discriminative power are significant to separate the target from the background, we propose a novel online tracking algorithm with two stage sparse optimization to jointly minimize the target reconstruction error and maximize the discriminative power. As the target template and discriminative features usually have temporal and spatial relationship, dynamic group sparsity (DGS) is utilized in our algorithm. The proposed method is compared with three state-of-art trackers using five public challenging sequences, which exhibit appearance changes, heavy occlusions, and pose variations. Our algorithm is shown to outperform these methods.

11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 2010.

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