GlobalTrack: A Simple and Strong Baseline for Long-Term Tracking
Open accessAbstract
A key capability of a long-term tracker is to search for targets in very large areas (typically the entire image) to handle possible target absences or tracking failures. However, currently there is a lack of such a strong baseline for global instance search. In this work, we aim to bridge this gap. Specifically, we propose GlobalTrack, a pure global instance search based tracker that makes no assumption on the temporal consistency of the target's positions and scales. GlobalTrack is developed based on two-stage object detectors, and it is able to perform full-image and multi-scale search of arbitrary instances with only a single query as the guide. We further propose a cross-query loss to improve the robustness of our approach against distractors. With no online learning, no punishment on position or scale changes, no scale smoothing and no trajectory refinement, our pure global instance search based tracker achieves comparable, sometimes much better performance on four large-scale tracking benchmarks (i.e., 52.1% AUC on LaSOT, 63.8% success rate on TLP, 60.3% MaxGM on OxUvA and 75.4% normalized precision on TrackingNet), compared to state-of-the-art approaches that typically require complex post-processing. More importantly, our tracker runs without cumulative errors, i.e., any type of temporary tracking failures will not affect its performance on future frames, making it ideal for long-term tracking. We hope this work will be a strong baseline for long-term tracking and will stimulate future works in this area.
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Previous long-term tracking methods usually consist of multiple components, such as base trackers, target verification modules, and global detectors.
“Specifically, previous methods usually consist of multiple components, such as base trackers [9, 50], target verification modules [21], and global detectors [40, 19].”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [19]
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Object Tracking via Spatial-Temporal Memory Network
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Global Tracking via Ensemble of Local Trackers
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A Unified Transformer-based Tracker for Anti-UAV Tracking
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CMAT: Integrating Convolution Mixer and Self-Attention for Visual Tracking
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All in One: Exploring Unified Vision-Language Tracking with Multi-Modal Alignment
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QueryTrack: Joint-Modality Query Fusion Network for RGBT Tracking
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SiamST: Siamese network with spatio-temporal awareness for object tracking
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Learning Modality Complementary Features with Mixed Attention Mechanism for RGB-T Tracking
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Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy
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Multi-modal multi-task feature fusion for RGBT tracking
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Online Learning Samples and Adaptive Recovery for Robust RGB-T Tracking
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Switch and Refine: A Long-Term Tracking and Segmentation Framework
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Effective Local and Global Search for Fast Long-Term Tracking
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Beyond SOT: Tracking Multiple Generic Objects at Once
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Memory Network With Pixel-Level Spatio-Temporal Learning for Visual Object Tracking
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Global Instance Tracking: Locating Target More Like Humans
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Self-citationTDIOT: Target-Driven Inference for Deep Video Object Tracking
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A joint local–global search mechanism for long-term tracking with dynamic memory network
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Visual Tracking by TridentAlign and Context Embedding
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SiamCAF: Complementary Attention Fusion-Based Siamese Network for RGBT Tracking
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A Real-time Anti-distractor Infrared UAV Tracker with Channel Feature Refinement Module
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Combining complementary trackers for enhanced long-term visual object tracking
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Is First Person Vision Challenging for Object Tracking?
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Long-term Visual Tracking: Review and Experimental Comparison
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An Anti-UAV Long-Term Tracking Method with Hybrid Attention Mechanism and Hierarchical Discriminator
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Siamese-DETR for Generic Multi-Object Tracking
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Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals
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Spatio-temporal hierarchical feature transformer for UAV object tracking
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Searching Region-Free and Template-Free Siamese Network for Tracking Drones in TIR Videos
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Joint Spatio-Temporal Similarity and Discrimination Learning for Visual Tracking
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Target-Aware State Estimation for Visual Tracking
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Rank-Based Verification for Long-Term Face Tracking in Crowded Scenes
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Learning Spatio-Temporal Attention Based Siamese Network for Tracking UAVs in the Wild
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STFTrack: Spatio-Temporal-Focused Siamese Network for Infrared UAV Tracking
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RTSformer: A Robust Toroidal Transformer With Spatiotemporal Features for Visual Tracking
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STMTrack: Template-free Visual Tracking with Space-time Memory NetworksPreprint
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LTST: Long-term segmentation tracker with memory attention network
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Robust Online Tracking With Meta-Updater
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Modality Meets Long-Term Tracker: A Siamese Dual Fusion Framework for Tracking UAV
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A spatial–temporal contexts network for object tracking
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Unified Transformer With Isomorphic Branches for Natural Language Tracking
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BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision
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Self-citationCoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking
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Optimizing intrinsic representation for tracking
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Hierarchical memory-guided long-term tracking with meta transformer inquiry network
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- Authors
- Huang, L., Zhao, X., & Huang, K.
- Affiliations
- Institute of Automation (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 07
- Pages
- 11037-11044
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2997896013
- Cited by
- 279
- References
- 36
- Citations by year
- 2026: 16 · 2025: 37 · 2024: 58 · 2023: 56 · 2022: 59 · 2021: 43 · 2020: 10
- FWCI
- 12.96
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61602485, 61673375)
- National Key Research and Development Program of China (2016YFB1001005)
- 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
- long-term object tracking, distractor robustness
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