NT-VOT211: A Large-Scale Benchmark for Night-Time Visual Object Tracking
Liu, Yu Mahmood, Arif Khan, Muhammad Haris
Liu, Yu
Mahmood, Arif
Khan, Muhammad Haris
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Many current visual object tracking benchmarks such as OTB100, NfS, UAV123, LaSOT, and GOT-10K, predominantly contain day-time scenarios while the challenges posed by the night-time has been less investigated. It is primarily because of the lack of a large-scale, well-annotated night-time benchmark for rigorously evaluating tracking algorithms. To this end, this paper presents NT-VOT211, a new benchmark tailored for evaluating visual object tracking algorithms in the challenging night-time conditions. NT-VOT211 consists of 211 diverse videos, offering 211,000 well-annotated frames with 8 attributes including camera motion, deformation, fast motion, motion blur, tiny target, distractors, occlusion and out-of-view. To the best of our knowledge, it is the largest night-time tracking benchmark to-date that is specifically designed to address unique challenges such as adverse visibility, image blur, and distractors inherent to night-time tracking scenarios. Through a comprehensive analysis of results obtained from 42 diverse tracking algorithms on NT-VOT211, we uncover the strengths and limitations of these algorithms, highlighting opportunities for enhancements in visual object tracking, particularly in environments with suboptimal lighting. Besides, a leaderboard for revealing performance rankings, annotation tools, comprehensive meta-information and all the necessary code for reproducibility of results is made publicly available. We believe that our NT-VOT211 benchmark will not only be instrumental in facilitating field deployment of VOT algorithms, but will also help VOT enhancements and it will unlock new real-world tracking applications. Our dataset and other assets can be found at: https://github.com/LiuYuML/NV-VOT211.
Citation
Y. Liu, A. Mahmood, and M. H. Khan, “NT-VOT211: A Large-Scale Benchmark for Night-Time Visual Object Tracking,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 15473, pp. 314–332, Jan. 2025, doi: 10.1007/978-981-96-0901-7_19.
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference
Asian Conference on Computer Vision (AACV)
Keywords
Low-light VOT, Low-visibility VOT, Night-time Tracking, Single object tracking, Visual object tracking benchmark
Subjects
Source
Asian Conference on Computer Vision (AACV)
Publisher
Springer Nature