The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Vision-Based Analysis of Small Groups in Pedestrian Crowds
Abstract
Building upon state-of-the-art algorithms for pedestrian detection and multi-object tracking, and inspired by sociological models of human collective behavior, we automatically detect small groups of individuals who are traveling together. These groups are discovered by bottom-up hierarchical clustering using a generalized, symmetric Hausdorff distance defined with respect to pairwise proximity and velocity. We validate our results quantitatively and qualitatively on videos of real-world pedestrian scenes. Where human-coded ground truth is available, we find substantial statistical agreement between our results and the human-perceived small group structure of the crowd. Results from our automated crowd analysis also reveal interesting patterns governing the shape of pedestrian groups. These discoveries complement current research in crowd dynamics, and may provide insights to improve evacuation planning and real-time situation awareness during public disturbances.
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
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120
Social force model for pedestrian dynamics
Helbing, D., & Molnár, P. (1995) · Physical Review E · doi:10.1103/physreve.51.4282 · Cited by 6,845
Objective Criteria for the Evaluation of Clustering Methods
Rand, W. M. (1971) · Journal of the American Statistical Association · doi:10.1080/01621459.1971.10482356 · Cited by 6,566
Learning patterns of activity using real-time tracking
Stauffer, C., & Grimson, W. E. L. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868677 · Cited by 3,248
An algorithm for tracking multiple targets
Reid, D. (1979) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1979.1102177 · Cited by 3,059
Statistical shape analysis: clustering, learning, and testing
Srivastava, A., Joshi, S. H., Mio, W., & Liu, X. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.86 · Cited by 2,727
A survey of advances in vision-based human motion capture and analysis
Moeslund, T. B., Hilton, A., & Krüger, V. (2006) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.08.002 · Cited by 2,541
Dantzer, R. (1990) · Neuropsychologia · doi:10.1016/0028-3932(90)90065-v · Cited by 2,354
Cited by
Applications of machine learning in animal behaviour studies
Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017) · Animal Behaviour · doi:10.1016/j.anbehav.2016.12.005 · Cited by 505
Space-time representation of people based on 3D skeletal data: A review
Han, F., Reily, B., Hoff, W., & Zhang, H. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.01.011 · Cited by 316
Detecting Coherent Groups in Crowd Scenes by Multiview Clustering
Wang, Q., Chen, M., Nie, F., & Li, X. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2875002 · Cited by 299
Scene-Independent Group Profiling in Crowd
Shao, J., Loy, C. C., & Wang, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.285 · Cited by 254
The more the merrier: Analysing the affect of a group of people in images
Dhall, A., Joshi, J., Sikka, K., Goecke, R., & Sebe, N. (2015) · 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2015.7163151 · Cited by 93
Metadata
- Authors
- Ge, W., Collins, R. T., & Ruback, R. B.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2012-05
- Volume
- 34
- Issue
- 5
- Pages
- 1003-1016
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2036721747
- Cited by
- 358
- References
- 61
- FWCI
- 17.25
- Type
- journal article
- 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: Vision-Based Analysis of Small Groups in Pedestrian Crowds. https://app.cochat.ai/papers/10.1109/tpami.2011.176
- The paper
- Ge, W., Collins, R. T., & Ruback, R. B. (2012). Vision-Based Analysis of Small Groups in Pedestrian Crowds. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2011.176
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.
