An effective visual tracking approach under low-light environments

Hanyu Jin; Zeyuan Wang; Rongtai Cai

doi:10.1117/12.3019529

25 March 2024 An effective visual tracking approach under low-light environments

Hanyu Jin, Zeyuan Wang, Rongtai Cai

Proceedings Volume 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023); 1308918 (2024) https://doi.org/10.1117/12.3019529
Event: Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 2023, Suzhou, China

Abstract

The performance of visual object detection, tracking, and recognition algorithms significantly degrade under low-light environments. To address this problem, we propose a simple yet effective approach for visual tracking under low-light environments. First, we detect and enhance low light images using Retinex technology, which boosts the contrast of the images to facilitate our algorithms to extract visual features for tracking. Then, we apply the kernelized correlation Filter (KCF) to track the object and refine the tracking performance by instance segmentation and bounding box optimization using Mask R-CNN. At last, we introduce re-detection into the proposed algorithm to enable the tracker to catch the object while it appears again after heavy occlusion or out-of-view motion. Consequently, the proposed algorithm is suitable for long-term tracking. Experiments on VOT2016, OTB50, and UAV20L show that the proposed algorithm largely improves the tracking success rate and precision, and the running time meets the need for real-time applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hanyu Jin, Zeyuan Wang, and Rongtai Cai "An effective visual tracking approach under low-light environments", Proc. SPIE 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 1308918 (25 March 2024); https://doi.org/10.1117/12.3019529

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