The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras
Open accessAbstract
When viewed from a system of multiple cameras with non-overlapping fields of view, the appearance of an object in one camera view is usually very different from its appearance in another camera view due to the differences in illumination, pose and camera parameters. In order to handle the change in observed colors of an object as it moves from one camera to another, we show that all brightness transfer functions from a given camera to another camera lie in a low dimensional subspace and demonstrate that this subspace can be used to compute appearance similarity. In the proposed approach, the system learns the subspace of inter-camera brightness transfer functions in a training phase during which object correspondences are assumed to be known. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both location and appearance cues. We evaluate the proposed method under several real world scenarios obtaining encouraging results.
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
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
Cited by
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
Li, W., Zhao, R., Xiao, T., & Wang, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.27 · Cited by 2,611
Person Re-identification by Descriptive and Discriminative Classification
Hirzer, M., Beleznai, C., Roth, P. M., & Bischof, H. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-21227-7_9 · Cited by 961
Deep Metric Learning for Person Re-identification
Yi, D., Lei, Z., Liao, S., & Li, S. Z. (2014) · 2014 22nd International Conference on Pattern Recognition · doi:10.1109/icpr.2014.16 · Cited by 931
Reidentification by Relative Distance Comparison
Zheng, W.-S., Gong, S., & Xiang, T. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.138 · Cited by 719
Intelligent multi-camera video surveillance: A review
Wang, X. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.07.005 · Cited by 694
Metadata
- Authors
- Javed, O., Shafique, K., & Shah, M.
- Venue
- 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
- Publisher
- IEEE
- Published
- 2005-07-27
- Volume
- 2
- Pages
- 26-33
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2115569382
- Cited by
- 289
- References
- 1
- FWCI
- 12.50
- 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: Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras. https://app.cochat.ai/papers/10.1109/cvpr.2005.71
- The paper
- Javed, O., Shafique, K., & Shah, M. (2005). Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). https://doi.org/10.1109/cvpr.2005.71
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.
