The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Forward-Backward Error: Automatic Detection of Tracking Failures
Open accessAbstract
This paper proposes a novel method for tracking failure detection. The detection is based on the Forward-Backward error, i.e. the tracking is performed forward and backward in time and the discrepancies between these two trajectories are measured. We demonstrate that the proposed error enables reliable detection of tracking failures and selection of reliable trajectories in video sequences. We demonstrate that the approach is complementary to commonly used normalized cross-correlation (NCC). Based on the error, we propose a novel object tracker called Median Flow. State-of-the-art performance is achieved on challenging benchmark video sequences which include non-rigid objects.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Machine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555
Cited by
Detection and tracking meet drones challenge
Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3119563 · Cited by 1,336
Learning Correspondence From the Cycle-Consistency of Time
Wang, X., Jabri, A., & Efros, A. A. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00267 · Cited by 445
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
Jain, A., Koppula, H. S., Raghavan, B., Soh, S., & Saxena, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.364 · Cited by 274
Computer vision based target-free 3D vibration displacement measurement of structures
Shao, Y., Li, L., Li, J., An, S., & Hao, H. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.113040 · Cited by 161
Improving Multiple Pedestrian Tracking by Track Management and Occlusion Handling
Stadler, D., & Beyerer, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01081 · Cited by 147
Metadata
- Authors
- Kalal, Z., Mikolajczyk, K., & Matas, J.
- Venue
- 2010 20th International Conference on Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-08
- Pages
- 2756-2759
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W2165737454
- Cited by
- 818
- References
- 1
- FWCI
- 17.87
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Forward-Backward Error: Automatic Detection of Tracking Failures. https://app.cochat.ai/papers/10.1109/icpr.2010.675
- The paper
- Kalal, Z., Mikolajczyk, K., & Matas, J. (2010). Forward-Backward Error: Automatic Detection of Tracking Failures. 2010 20th International Conference on Pattern Recognition. https://doi.org/10.1109/icpr.2010.675
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
