Abstract

Despite much success in the application of sparse representation to object tracking, most of the existing sparse-representation-based tracking methods are still not robust enough for challenges such as pose variations, illumination changes, occlusions, and background distractions. In this paper, we propose a robust object-tracking algorithm via local discriminative sparse representation. The key idea in our method is to develop what we believe is a novel local discriminative sparse representation method for object appearance modeling, which can be helpful to overcome issues such as appearance variations and occlusions. Then a robust tracker based on the local discriminative sparse appearance model is proposed to track the object over time. Additionally, an online dictionary update strategy is introduced in our approach for further robustness. Experimental results on challenging sequences demonstrate the effectiveness and robustness of our proposed method.

© 2017 Optical Society of America

Object tracking based on incremental Bi-2DPCA learning with sparse structure

Bendu Bai, Ying Li, Jiulun Fan, Chris Price, and Qiang Shen
Appl. Opt. 54(10) 2897-2907 (2015)

Multi-class remote sensing object recognition based on discriminative sparse representation

Xin Wang, Siqiu Shen, Chen Ning, Fengchen Huang, and Hongmin Gao
Appl. Opt. 55(6) 1381-1394 (2016)

Real-time infrared target tracking based on ℓ₁ minimization and compressive features

Ying Li, Pengcheng Li, and Qiang Shen
Appl. Opt. 53(28) 6518-6526 (2014)

Online multi-object tracking with efficient track drift and fragmentation handling

Jaeyong Ju, Daehun Kim, Bonhwa Ku, David K. Han, and Hanseok Ko
J. Opt. Soc. Am. A 34(2) 280-293 (2017)

Robust block sparse discriminative classification framework

Yang Liu, Chenyu Liu, Yufang Tang, Haixu Liu, Shuxin Ouyang, and Xueming Li
J. Opt. Soc. Am. A 31(12) 2806-2813 (2014)