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GSM: Graph Similarity Model for Multi-Object Tracking
Open accessAbstract
The popular tracking-by-detection paradigm for multi-object tracking (MOT) focuses on solving data association problem, of which a robust similarity model lies in the heart. Most previous works make effort to improve feature representation for individual object while leaving the relations among objects less explored, which may be problematic in some complex scenarios. In this paper, we focus on leveraging the relations among objects to improve robustness of the similarity model. To this end, we propose a novel graph representation that takes both the feature of individual object and the relations among objects into consideration. Besides, a graph matching module is specially designed for the proposed graph representation to alleviate the impact of unreliable relations. With the help of the graph representation and the graph matching module, the proposed graph similarity model, named GSM, is more robust to the occlusion and the targets sharing similar appearance. We conduct extensive experiments on challenging MOT benchmarks and the experimental results demonstrate the effectiveness of the proposed method.
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“Furthermore, the frame association with track queries avoids postprocessing with heuristic greedy matching procedures [69] or additional graph optimization [29].”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [29]
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“These approaches achieve track association implicitly, but provide top performance only by relying either on additional graph optimization [6, 29] or motion and appearance models [4].”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [29]
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“Third, we apply the proposed graph similarity model (GSM) to Tracktor [Bergmann et al. , 2019], the current state-of-the-art online MOT tracker, and achieve the best performance on MOT benchmarks [Milan et al. , 2016] in most metrics including MOTA and IDF1.”
MethodTracking Without Bells and Whistles
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Joint Re-Detection and Re-Identification for Multi-Object Tracking
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Autoregressive Temporal Modeling for Advanced Tracking-by-Diffusion
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Metadata
- Authors
- Liu, Q., Chu, Q., Liu, B., & Yu, N.
- Affiliations
- University of Science and Technology of China (CN)
- Corresponding author
- Chu, Qi
- Venue
- Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
- Published
- 2020-07
- Pages
- 530-536
- OpenAlex
- W3034240185
- Cited by
- 123
- References
- 27
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 23 · 2023: 28 · 2022: 22 · 2021: 27
- FWCI
- 5.94
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Funders
- Chinese Academy of Sciences (CXJJ-17S044)
- Fundamental Research Funds for the Central Universities (WK2100330002, WK3480000005)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- multi-target tracking, graph representation, object relations, graph matching, data association, tracking-by-detection, occlusion robustness, visual similarity
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