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LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search
Open accessAbstract
Object tracking has achieved significant progress over the past few years. However, state-of-the-art trackers become increasingly heavy and expensive, which limits their deployments in resource-constrained applications. In this work, we present LightTrack, which uses neural architecture search (NAS) to design more lightweight and efficient object trackers. Comprehensive experiments show that our LightTrack is effective. It can find trackers that achieve superior performance compared to handcrafted SOTA trackers, such as SiamRPN++ [30] and Ocean [56], while using much fewer model Flops and parameters. Moreover, when deployed on resource-constrained mobile chipsets, the discovered trackers run much faster. For example, on Snapdragon 845 Adreno GPU, LightTrack runs 12× faster than Ocean, while using 13× fewer parameters and 38× fewer Flops. Such improvements might narrow the gap between academic models and industrial deployments in object tracking task. LightTrack is released at here.
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Wang, Y., Zhou, L., An, Z., Sun, L., Hu, M., & Wang, J. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.104725 · Cited by 1
LKTrack: a novel tracking framework with large kernel network
Zhang, H., Lin, H., Yuan, D., Song, J., Liu, H., & Yang, Y. (2026) · Multimedia Systems · doi:10.1007/s00530-025-02135-3 · Cited by 1
ELASTIC: Efficient Once for All Iterative Search for Object Detection on Microcontrollers
Tran, T., Lin, Q., & Hu, B. (2026) · IEEE Transactions on Computers · doi:10.1109/tc.2026.3678184 · Cited by 1
Ocean: Object-aware Anchor-free Tracking with Matching-relation Learning
Hu, W., Zhang, Z., Li, B., Peng, H., & Maybank, S. J. (2026) · Machine Intelligence Research · doi:10.1007/s11633-026-1634-0 · Cited by 2
Self-citationZhu, H., Zhang, H., Chen, W., He, Z., Wu, X., & Liang, S. (2026) · Applied Sciences · doi:10.3390/app16094550 · Cited by 1
Metadata
- Authors
- Yan, B., Peng, H., Wu, K., Wang, D., Fu, J., & Lu, H.
- Affiliations
- Dalian University of Technology (CN)
- Microsoft Research Asia (China) (CN)
- Sun Yat-sen University (CN)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 15175-15184
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3173344965
- Cited by
- 277
- References
- 59
- Citations by year
- 2026: 32 · 2025: 72 · 2024: 77 · 2023: 50 · 2022: 40 · 2021: 6
- FWCI
- 14.15
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object tracking, lightweight neural networks, neural architecture search, resource-constrained deployment, mobile devices, model compression
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