Detect to Track and Track to Detect
Open accessAbstract
Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this paper we propose a ConvNet architecture that jointly performs detection and tracking, solving the task in a simple and effective way. Our contributions are threefold: (i) we set up a ConvNet architecture for simultaneous detection and tracking, using a multi-task objective for frame-based object detection and across-frame track regression; (ii) we introduce correlation features that represent object co-occurrences across time to aid the ConvNet during tracking; and (iii) we link the frame level detections based on our across-frame tracklets to produce high accuracy detections at the video level. Our ConvNet architecture for spatiotemporal object detection is evaluated on the large-scale ImageNet VID dataset where it achieves state-of-the-art results. Our approach provides better single model performance than the winning method of the last ImageNet challenge while being conceptually much simpler. Finally, we show that by increasing the temporal stride we can dramatically increase the tracker speed.
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TrackFormer's identity preservation performance on MOT17 public detections is only surpassed by [47] and offline methods that process entire sequences at once.
“Our identity preservation performance is only surpassed by [47] and offline methods which benefit from the processing of entire sequences at once.”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [47]
The Introduction and Results discuss D&T on ImageNet VID, reporting detection mAP and online versus noncausal rescoring. Our copy does not address TrackFormer, MOT17 public detections, or the claimed identity-preservation ranking (searched: MOT17, identity preservation, offline methods). (LLM, conf=0.78)
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- Authors
- Feichtenhofer, C., Pinz, A., & Zisserman, A.
- Affiliations
- Austrian Academy of Sciences (AT)
- VRVis GmbH (Austria) (AT)
- University of Oxford (GB)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 3057-3065
- OpenAlex
- W2756784878
- Cited by
- 595
- References
- 44
- Citations by year
- 2026: 8 · 2025: 36 · 2024: 53 · 2023: 79 · 2022: 84 · 2021: 119 · 2020: 100 · 2019: 76 · 2018: 35 · 2017: 5
- FWCI
- 17.00
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Austrian Science Fund (P 27076)
- Engineering and Physical Sciences Research Council (EP/M013774/1, EP/M013774/)
- 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
- video object tracking, object detection, joint detection and tracking, multi-task learning, correlation features, object co-occurrence, tracklets
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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