Fine scale estimation for correlation filter tracking

Yanchuan Wang; Hongtao Yu; Shaomei Li; Chao Gao; Hongxin Zhi; Cha Zheng; Qian Xu

doi:10.1117/12.2501792

26 July 2018 Fine scale estimation for correlation filter tracking

Yanchuan Wang, Hongtao Yu, Shaomei Li, Chao Gao, Hongxin Zhi, Cha Zheng, Qian Xu

Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 1082816 (2018) https://doi.org/10.1117/12.2501792
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China

Abstract

Focusing on the issue that Correlation Filter Trackers has poor performance in scale variations, a fine scale estimation approach is proposed. Firstly, we train a scale correlation filter using the target initial state. Secondly, the target is segmented according to its shape and then two subgraph correlation filters are respectively established. During tracking, we judge the trend of scale changes by the relative position changes of the subgraphs and the weights of the scale samples are offset. In this way, we obtain the coarse scale estimation of the target. Finally, we use Newton method to accurately estimate the scale of the target. Experiments show that the algorithm achieves more accurate scale estimation and effectively improves the tracking success rate.

Citation Download Citation

Yanchuan Wang, Hongtao Yu, Shaomei Li, Chao Gao, Hongxin Zhi, Cha Zheng, and Qian Xu "Fine scale estimation for correlation filter tracking", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 1082816 (26 July 2018); https://doi.org/10.1117/12.2501792

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