Papers › Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework

Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework

22 Mar 2022arXiv:2203.11991archive 2025-07-28

Botao Ye, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen

The current popular two-stream, two-stage tracking framework extracts the template and the search region features separately and then performs relation modeling, thus the extracted features lack the awareness of the target and have limited target-background discriminability. To tackle the above issue, we propose a novel one-stream tracking (OSTrack) framework that unifies feature learning and relation modeling by bridging the template-search image pairs with bidirectional information flows. In this way, discriminative target-oriented features can be dynamically extracted by mutual guidance. Since no extra heavy relation modeling module is needed and the implementation is highly parallelized, the proposed tracker runs at a fast speed. To further improve the inference efficiency, an in-network candidate early elimination module is proposed based on the strong similarity prior calculated in the one-stream framework. As a unified framework, OSTrack achieves state-of-the-art performance on multiple benchmarks, in particular, it shows impressive results on the one-shot tracking benchmark GOT-10k, i.e., achieving 73.7% AO, improving the existing best result (SwinTrack) by 4.3\%. Besides, our method maintains a good performance-speed trade-off and shows faster convergence. The code and models are available at https://github.com/botaoye/OSTrack.

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botaoye/ostrack officialmentioned in papermentioned on GitHubpytorchMIT report

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OSTrack botaoye/OSTrack/lib/models/ostrack/ostrack.py official repository unverified MIT (permissive) · 98484d88923ea9d9 · report

Tasks

Object TrackingVideo Object TrackingVisual Object TrackingVisual Tracking

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Object Tracking COESOT OSTrack Precision Rate 66.6 #11 of 12 Archive leaderboard report
Object Tracking COESOT OSTrack Success Rate 59.0 #11 of 12 Archive leaderboard report
Video Object Tracking NT-VOT211 OSTrack-384 AUC 38.59 #8 of 43 Archive leaderboard report
Video Object Tracking NT-VOT211 OSTrack-384 Precision 53.06 #8 of 43 Archive leaderboard report
Visual Object Tracking GOT-10k OSTrack-384 Average Overlap 73.7 #23 of 42 Archive leaderboard report
Visual Object Tracking GOT-10k OSTrack-384 Success Rate 0.5 83.2 #23 of 42 Archive leaderboard report
Visual Object Tracking GOT-10k OSTrack-384 Success Rate 0.75 70.8 #23 of 42 Archive leaderboard report
Visual Object Tracking LaSOT OSTrack-384 AUC 71.1 #24 of 46 Archive leaderboard report
Visual Object Tracking LaSOT OSTrack-384 Normalized Precision 81.1 #24 of 46 Archive leaderboard report
Visual Object Tracking LaSOT OSTrack-384 Precision 77.6 #24 of 46 Archive leaderboard report
Visual Object Tracking LaSOT-ext OSTrack AUC 50.6 #16 of 18 Archive leaderboard report
Visual Object Tracking LaSOT-ext OSTrack Normalized Precision 61.3 #16 of 18 Archive leaderboard report
Visual Object Tracking LaSOT-ext OSTrack Precision 57.6 #16 of 18 Archive leaderboard report
Visual Object Tracking TrackingNet OSTrack-384 Accuracy 83.9 #20 of 40 Archive leaderboard report
Visual Object Tracking TrackingNet OSTrack-384 Normalized Precision 88.5 #20 of 40 Archive leaderboard report
Visual Object Tracking TrackingNet OSTrack-384 Precision 83.2 #20 of 40 Archive leaderboard report
Visual Object Tracking UAV123 OSTrack -384 AUC 0.707 #7 of 16 Archive leaderboard report
Visual Tracking TNL2K OSTrack AUC 55.9 #4 of 6 Archive leaderboard report

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Methods

AO

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