HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912393939910656 |
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| author | Di Bella, Leandro Lyu, Yangxintong Cornelis, Bruno Munteanu, Adrian |
| author_facet | Di Bella, Leandro Lyu, Yangxintong Cornelis, Bruno Munteanu, Adrian |
| contents | The evolution of Advanced Driver Assistance Systems (ADAS) has increased the need for robust and generalizable algorithms for multi-object tracking. Traditional statistical model-based tracking methods rely on predefined motion models and assumptions about system noise distributions. Although computationally efficient, they often lack adaptability to varying traffic scenarios and require extensive manual design and parameter tuning. To address these issues, we propose a novel 3D multi-object tracking approach for vehicles, HybridTrack, which integrates a data-driven Kalman Filter (KF) within a tracking-by-detection paradigm. In particular, it learns the transition residual and Kalman gain directly from data, which eliminates the need for manual motion and stochastic parameter modeling. Validated on the real-world KITTI dataset, HybridTrack achieves 82.72% HOTA accuracy, significantly outperforming state-of-the-art methods. We also evaluate our method under different configurations, achieving the fastest processing speed of 112 FPS. Consequently, HybridTrack eliminates the dependency on scene-specific designs while improving performance and maintaining real-time efficiency. The code is publicly available at: https://github.com/leandro-svg/HybridTrack. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01275 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking Di Bella, Leandro Lyu, Yangxintong Cornelis, Bruno Munteanu, Adrian Computer Vision and Pattern Recognition Robotics The evolution of Advanced Driver Assistance Systems (ADAS) has increased the need for robust and generalizable algorithms for multi-object tracking. Traditional statistical model-based tracking methods rely on predefined motion models and assumptions about system noise distributions. Although computationally efficient, they often lack adaptability to varying traffic scenarios and require extensive manual design and parameter tuning. To address these issues, we propose a novel 3D multi-object tracking approach for vehicles, HybridTrack, which integrates a data-driven Kalman Filter (KF) within a tracking-by-detection paradigm. In particular, it learns the transition residual and Kalman gain directly from data, which eliminates the need for manual motion and stochastic parameter modeling. Validated on the real-world KITTI dataset, HybridTrack achieves 82.72% HOTA accuracy, significantly outperforming state-of-the-art methods. We also evaluate our method under different configurations, achieving the fastest processing speed of 112 FPS. Consequently, HybridTrack eliminates the dependency on scene-specific designs while improving performance and maintaining real-time efficiency. The code is publicly available at: https://github.com/leandro-svg/HybridTrack. |
| title | HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2501.01275 |