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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Decode-MOT: How Can We Hurdle Frames to Go Beyond Tracking-by-Detection?
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Hyperspectral Video Tracking With Spectral–Spatial Fusion and Memory Enhancement
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Visual and Language Collaborative Learning for RGBT Object Tracking
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PHTrack: Prompting for Hyperspectral Video Tracking
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Resource-Efficient RGBD Aerial Tracking
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Part-Aware Framework for Robust Object Tracking
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Correlation-Embedded Transformer Tracking: A Single-Branch Framework
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Rank-Based Filter Pruning for Real-Time UAV Tracking
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EMTrack: Efficient Multimodal Object Tracking
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Self-citationLightweight full-convolutional siamese tracker
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NiDR: Nighttime Aerial Tracking via Decoupled Representations
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Fisher Pruning for Real-Time UAV Tracking
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CLASH: Complementary Learning With Neural Architecture Search for Gait Recognition
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Exploring Dynamic Transformer for Efficient Object Tracking
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Self-citationSiamSTC: Updatable Siamese tracking network via Spatio-Temporal Context
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Video-Based Detection of Freezing of Gait in Daily Clinical Practice in Patients With Parkinsonism
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A Transformer-based visual object tracker via learning immediate appearance change
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Siamese Object Tracking for Vision-Based UAM Approaching with Pairwise Scale-Channel Attention
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A Global-Local Tracking Framework Driven by Both Motion and Appearance for Infrared Anti-UAV
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DCFNet: Discriminant Correlation Filters Network for Visual Tracking
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Spatio-temporal matching for siamese visual tracking
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Bidirectional Interaction of CNN and Transformer Feature for Visual Tracking
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Divert More Attention to Vision-Language Object Tracking
Guo, M., Zhang, Z., Jing, L., Ling, H., & Fan, H. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3409078 · Cited by 15 · see citing works
A transformer based visual tracker with restricted token interaction and knowledge distillation
Liu, N., & Zhang, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112736 · Cited by 15 · see citing works
Tracking Small and Fast Moving Objects: A Benchmark
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CTIFTrack: Continuous Temporal Information Fusion for object track
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Transformer-based moving target tracking method for Unmanned Aerial Vehicle
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Scale-Aware Siamese Object Tracking for Vision-Based UAM Approaching
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An Occlusion-Aware Tracker With Local-Global Features Modeling in UAV Videos
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Enabling deformation slack in tracking with temporally even correlation filters
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Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking
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Online Multi-Scale Classification and Global Feature Modulation for Robust Visual Tracking
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Exploring the complementarity between convolution and transformer matching for visual tracking
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Online learning discriminative sparse convolution networks for robust UAV object tracking
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Spatial-Temporal Contextual Aggregation Siamese Network for UAV Tracking
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STAR: A Unified Spatiotemporal Fusion Framework for Satellite Video Object Tracking
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Probabilistic Assignment With Decoupled IoU Prediction for Visual Tracking
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Joint Response and Background Learning for UAV Visual Tracking
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A lightweight and efficient one-stage detection framework
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SiamHOT: Siamese High-Order Transformer for Aerial Tracking
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Deep Learning for Single-object Tracking: A Survey
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- 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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