Scale adaptive part-based tracking method using multiple correlation filters

Hongyu Chen; Haibo Luo; Faling Chen; Bin Hui; Zheng Chang

doi:10.1117/1.OE.58.5.053106

27 May 2019 Scale adaptive part-based tracking method using multiple correlation filters

Hongyu Chen, Haibo Luo, Faling Chen, Bin Hui, Zheng Chang

Author Affiliations +

Optical Engineering, Vol. 58, Issue 5, 053106 (May 2019). https://doi.org/10.1117/1.OE.58.5.053106

Abstract

Visual tracking plays a significant role in computer vision. Although numerous tracking algorithms have shown promising results, target tracking remains a challenging task due to appearance changes caused by deformation, scale variation, and partial occlusion. Part-based methods have great potential in addressing the deformation and partial occlusion issues. Owing to the addition of multiple part trackers, most of these part-based trackers cannot run in real time. Correlation filters have been used in target tracking owing to their high efficiency. However, the correlation filter-based trackers face great problems dealing with occlusion, deformation, and scale variation. To better address the above-mentioned issues, we present a scale adaptive part-based tracking method using multiple correlation filters. Our proposed method utilizes the scale-adaptive tracker for both root and parts. The target location is determined by the responses of root tracker and part trackers collaboratively. To estimate the target scale more precisely, the root scale and each part scale are predicted with the sequential Monte Carlo framework. An adaptive weight joint confidence map is acquired by assigning proper weights to independent confidence maps. Experimental results on the publicly available OTB100 dataset demonstrate that our approach outperforms other state-of-the-art trackers.

Citation Download Citation

Hongyu Chen, Haibo Luo, Faling Chen, Bin Hui, and Zheng Chang "Scale adaptive part-based tracking method using multiple correlation filters," Optical Engineering 58(5), 053106 (27 May 2019). https://doi.org/10.1117/1.OE.58.5.053106

Received: 24 February 2019; Accepted: 2 May 2019; Published: 27 May 2019

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available