Tracking Without Bells and Whistles
Open accessAbstract
The problem of tracking multiple objects in a video sequence poses several challenging tasks. For tracking-by-detection, these include object re-identification, motion prediction and dealing with occlusions. We present a tracker (without bells and whistles) that accomplishes tracking without specifically targeting any of these tasks, in particular, we perform no training or optimization on tracking data. To this end, we exploit the bounding box regression of an object detector to predict the position of an object in the next frame, thereby converting a detector into a Tracktor. We demonstrate the potential of Tracktor and provide a new state-of-the-art on three multi-object tracking benchmarks by extending it with a straightforward re-identification and camera motion compensation. We then perform an analysis on the performance and failure cases of several state-of-the-art tracking methods in comparison to our Tracktor. Surprisingly, none of the dedicated tracking methods are considerably better in dealing with complex tracking scenarios, namely, small and occluded objects or missing detections. However, our approach tackles most of the easy tracking scenarios. Therefore, we motivate our approach as a new tracking paradigm and point out promising future research directions. Overall, Tracktor yields superior tracking performance than any current tracking method and our analysis exposes remaining and unsolved tracking challenges to inspire future research directions.
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Tracking-by-regression approaches achieve track association implicitly but provide top performance only by relying on additional graph optimization or motion and appearance models.
“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 [4]
Section 2.2 (p. 3) says bounding-box regression transfers a track’s identity automatically, supporting implicit association. Sections 2.3 (p. 4) and 3.1 (p. 5) describe motion and appearance-based re-identification extensions that improve performance. But the claim’s “only” overstates the source: the Abstract (p. 1) also reports superior performance for the tracker without tracking-specific training or optimization. The graph-optimization alternative is attributed to other citations, not this paper. (LLM, conf=0.86)
Experimental — accuracy not yet measuredTrackFormer's tracking-by-attention approach achieves top performance via global attention without relying on additional motion or appearance models.
“Our tracking-by-attention approach achieves top performance via global attention without relying on additional motion [4, 10] or appearance models [4, 8, 10].”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [4]
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Cited by
Sorted by citations
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“To solve this problem, several recent methods [1,60,74] integrate the detector and embedding model into a unified framework.”
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“Con-1312 cerning this issue, [144] proposes as alternative tracking-1313 by-regression, which can also be used for multiple-vehicle tracking to improve the overall system performance.”
BackgroundIsaac-Medina, B. K. S., Poyser, M., Organisciak, D., Willcocks, C. G., Breckon, T. P., & Shum, H. P. H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00142 · Cited by 9 · see citing works
- Authors
- Bergmann, P., Meinhardt, T., & Leal-Taixe, L.
- Affiliations
- Technical University of Munich (DE)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 941-951
- OpenAlex
- W2920942303
- Cited by
- 1,110
- References
- 50
- Citations by year
- 2026: 60 · 2025: 117 · 2024: 194 · 2023: 188 · 2022: 231 · 2021: 206 · 2020: 108 · 2019: 6
- FWCI
- 44.94
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- multi-target tracking, tracking-by-detection, bounding box regression, object detectors, object re-identification, camera motion compensation, occlusion handling, missing detection, small objects
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