The DOI matches its Crossref record.
A Viewpoint Invariant Approach for Crowd Counting
Abstract
This paper describes a viewpoint invariant learningbased method for counting people in crowds from a single camera. Our method takes into account feature normalization to deal with perspective projection and different camera orientation. The training features include edge orientation and blob size histograms resulted from edge detection and background subtraction. A density map that measures the relative size of individuals and a global scale measuring camera orientation are estimated and used for feature normalization. The relationship between the feature histograms and the number of pedestrians in the crowds is learned from labeled training data. Experimental results from different sites with different camera orientation demonstrate the performance and the potential of our method.
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
Cited by
Cross-scene crowd counting via deep convolutional neural networks
Zhang, C., Li, H., Wang, X., & Yang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298684 · Cited by 1,121
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
Idrees, H., Tayyab, M., Athrey, K., Zhang, D., Al-Maadeed, S., Rajpoot, N., & Shah, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_33 · Cited by 851
Drone-Based Object Counting by Spatially Regularized Regional Proposal Network
Hsieh, M.-R., Lin, Y.-L., & Hsu, W. H. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.446 · Cited by 547
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
Boominathan, L., Kruthiventi, S. S. S., & Babu, R. V. (2016) · Proceedings of the 24th ACM international conference on Multimedia · doi:10.1145/2964284.2967300 · Cited by 503
Vision-Based Analysis of Small Groups in Pedestrian Crowds
Ge, W., Collins, R. T., & Ruback, R. B. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.176 · Cited by 358
Metadata
- Authors
- Kong, D., Gray, D., & Tao, H.
- Venue
- 18th International Conference on Pattern Recognition (ICPR'06)
- Publisher
- IEEE
- Published
- 2006
- Pages
- 1187-1190
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W2121864252
- Cited by
- 251
- References
- 0
- FWCI
- 5.37
- Type
- conference paper
- Language
- en
- Open access
- closed
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: A Viewpoint Invariant Approach for Crowd Counting. https://app.cochat.ai/papers/10.1109/icpr.2006.197
- The paper
- Kong, D., Gray, D., & Tao, H. (2006). A Viewpoint Invariant Approach for Crowd Counting. 18th International Conference on Pattern Recognition (ICPR'06). https://doi.org/10.1109/icpr.2006.197
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.
