The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Crowd analysis: a survey
Open accessAbstract
In the year 1999 the world population reached 6 billion, doubling the previous census estimate of 1960. Recently, the United States Census Bureau issued a revised forecast for world population showing a projected growth to 9.4 billion by 2050 (US Census Bureau, http://www.census.gov/ipc/www/worldpop.html). Different research disci- plines have studied the crowd phenomenon and its dynamics from a social, psychological and computational standpoint respectively. This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach.
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
An algorithm for tracking multiple targets
Reid, D. (1979) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1979.1102177 · Cited by 3,059
Research works printed but not linked: 82
- Andrade, E., Fisher, R.: Simulation of crowd problems for computer vision. In: First International Workshop on Crowd Simulation, vol. 3, pp. 71–80 (2005)
- Andrade, E., Fisher, R.: Hidden Markov models for optical flow analysis in crowds. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06), vol. 01, pp. 460–463. IEEE Computer Society Washington, (2006) · DOI
- Andrade, E., Fisher, R.: Modelling crowd scenes for event detection. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06), vol. 01, pp. 175–178. IEEE Computer Society Washington, DC (2006) · DOI · Paper page
- Andrade, E.L., Blunsden, S., Fisher, R.B.: Performance analysis of event detection models in crowded scenes. In: Proceedings of Workshop on Towards Robust Visual Surveillance Techniques and Systems at Visual Information Engineering 2006, pp. 427–432. Bangalore, India (2006) · DOI
- Antonini, G., Bierlaire, M., Weber, M.: Simulation of pedestrian behaviour using a discrete choice model calibrated on actual motion data. In: 4th STRC Swiss Transport Research Conference. Ascona (2004)
- Antonini, G., Venegas, S., Thiran J.P., Bierlaire, M.: A discrete choice pedestrian behaviour model in visual tracking systems. In: Advanced Concepts for Intelligent Vision Systems, pp. 273–280. Brussels, Belgium (2004)
- Banarjee, S., Grosan, C., Abarha, A.: Emotional ant based modeling of crowd dynamics. In: Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC’05), pp. 279–286 (2005)
- Blackman, S.: Multiple hypothesis tracking for multiple target tracking. Aerospace Electron. Syst. Mag. IEEE 19(1), 5–18 (2004) · DOI · Paper page
- Boghossian, B., Velastin, S.: Motion-based machine vision techniques for the management of large crowds. In: The 6th IEEE International Conference on Electronics, Circuits and Systems, vol. 2 (1999)
- Brenner, M., Wijermans, N., Nussle, T., de Boer, B.: Simulating and controlling civilian crowds in robocup rescue. In: Proceedings of RoboCup 2005: Robot Soccer World Cup IX, Osaka (2005)
- Broggi, A., Bertozzi, M., Fascioli, A., Sechi, M.: Shape-based pedestrian detection. In: Proceedings of the IEEE Intelligent Vehicles Symposium 2000. Dearbon (MI), USA (2000)
- Brostow, G., Cipolla, R.: Unsupervised Bayesian detection of independent motion in crowds. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 594–601. IEEE Computer Society Washington, DC, USA (2006)
- Cai, Y., de Freitas, N., Little, J.J.: Robust visual tracking for multiple targets. In: European Conference on Computer Vision, LNCS, vol. 3954, pp. 107–118. Springer, Heidelberg (2006) · DOI · Paper page
- Chan, M.T., Hoogs, A., Bhotika, R., Perera, A., Schmiederer, J., Doretto, G.: Joint recognition of complex events and track matching. In: CVPR ’06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1615–1622. IEEE Computer Society, Washington, DC, USA (2006). doi:10.1109/CVPR.2006.160 · doi:10.1109/cvpr.2006.160
- Chang, T., Gong, S., Ong, E.: Tracking multiple people under occlusion using multiple cameras. In: British Machine Vision Conference, pp. 566–575 (2000) · DOI
- Cupillard, F., Bremond, F., Thonnat, M.: Behaviour recognition for individuals, groups of people and crowd. IEE Seminar Digests 7 (2003) · doi:10.1049/ic:20030033
- Cupillard, F., Bremond, F., Thonnat, M., INRIA, F.: Group behavior recognition with multiple cameras. Applications of Computer Vision, 2002 (WACV 2002). In: Proceedings of Sixth IEEE Workshop, pp. 177–183 (2002)
- Davies, A., Yin, J., Velastin, S.: Crowd monitoring using image processing. Electron. Commun. Eng. J. 7(1), 37–47 (1995) · DOI · Paper page
- Dong, L., Parameswaran, V., Ramesh, V., Zoghlami, I.: Fast Crowd Segmentation Using Shape Indexing. Rio de Janeiro, Brazil (2007)
- Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering (2000) · DOI · Paper page
- Elgammal, A., Davis, L.: Probabilistic framework for segmenting people under occlusion. In: Eighth IEEE International Conference on Computer Vision, 2001. Proceedings of ICCV 2001, vol. 2, pp. 145–152 (2001)
- Gabriel, P., Verly, J., Piater, J., Genon, A.: The state of the art in multiple object tracking under occlusion in video sequences. Advanced Concepts for Intell. Vis. Syst., pp. 166–173 (2003)
- Han, M., Xu, W., Tao, H., Gong, Y.: An algorithm for multiple object trajectory tracking. Computer Vision and Pattern Recognition, 2004. In: CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference, vol. 1 (2004)
- Heisele B., Woehler C. (1998) Motion-based recognition of pedestrians. In: Proceedings of Fourteenth International Conference on Pattern Recognition, 1998, vol. 2, pp. 1325–1330 (1998)
- Helbing, D.: Models for pedestrian behavior (1992). http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/9805089 · arXiv
- Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Lett. Nat. 407, 487–490 (2000) · DOI · Paper page
- Helbing, D., Molnár, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282–4286 (1995) · DOI · Paper page
- Helbing, D., Molnar, P.: Self-organization phenomena in pedestrian crowds (1997). http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/9806152 · arXiv
- Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst., Man Cybernet. C Appl. Rev. 34(3), 334–352 (2004) · DOI · Paper page
- Huang, C., Ai, H., Li, Y., Lao, S.: Vector boosting for rotation invariant multi-view face detection. In: Tenth IEEE Inter- national Conference on Computer Vision, vol. 1, pp. 446–453 (2005)
- Huang X., Li L., Sim T. (2004) Stereo-based human head detection from crowd scenes. In: International Conference on Image Processing, 2004. ICIP’04, Vol. 2, pp. 1353–1356
- Hughes, R.: A continuum theory for the flow of pedestrians. Trans. Res. B Methodol. 36(6), 507–535 (2002) · DOI · Paper page
- Isard, M., Blake, A.: A mixed-state CONDENSATION tracker with automatic model-switching. In: IEEE International Conference on Computer Vision, pp. 107–112 (1998). http://citeseer.ist.psu.edu/isard98mixedstate.html
- Kang, H., Kim, D., Bang, S.: Real-time multiple people tracking using competitive condensation. Proc. Int. Conf. Pattern Recogn. 1, 413–416 (2002)
- Karlsson, R., Gustafsson, F.: Monte Carlo data association for multiple target tracking. Target Tracking: Algorithms and Applications (Ref. No. 2001/174), IEE, vol. 1 (2001) · doi:10.1049/ic:20010239
- Khan, S.M., Shah, M.: A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: 9th European Conference on Computer Vision, LNCS, vol. 3954, pp. 133–146. Springer, Heidelberg (2006) · DOI · Paper page
- Khan, Z., Balch, T., Dellaert, F.: MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1805–1819 (2005) · DOI · Paper page
- Kim, K., Davis, L.S.: Multi-camera tracking and segmentation of occluded people on ground plane using search-guided particle filtering. In: European Conference on Computer Vision, LNCS, vol. 3953, pp. 98–109. Springer, Heidelberg (2006) · DOI
- Kirchner, A., Schadschneider, A.: Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Phys. A Stat. Mech. Appl. 312(1–2), 260–276 (2002) · DOI · Paper page
- Kirkland, J., Maciejewski, A.: A simulation of attempts to influence crowd dynamics. IEEE Int. Conf. Syst. Man Cybernet. 4328–4333 (2003)
- Koller-Meier, E., Ade, F.: Tracking multiple objects using the Condensation algorithm. Robot. Auton. Syst. 34(2-3), 93–105 (2001) · DOI
- Kong, D., Gray, D., Tao, H.: Counting Pedestrians in crowds using viewpoint invariant training. In: British Machine Vision Conference (2005) · DOI
- Kong, D., Gray, D., Tao, H.: A viewpoint invariant approach for crowd counting. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR’06), vol. 03, pp. 1187–1190 (2006) · DOI · Paper page
- Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recogn. CVPR 2005. 1 (2005)
- Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H.: Statistical learning of multi-view face detection. In: Proceedings of the 7th European Conference on Computer Vision-Part IV, pp. 67–81. Springer, London (2002) · DOI · Paper page
- Lin, S., Chen, J., Chao, H.: Estimation of number of people in crowded scenes using perspective transformation. IEEE Trans. Syst. Man Cybernet. A 31(6), 645–654 (2001) · DOI · Paper page
- Ma, R., Li, L., Huang, W., Tian, Q.: On pixel count based crowd density estimation for visual surveillance. IEEE Conf. Cybernet. Intell. Syst. 1 (2004)
- Marana, A., da Costa, L., Lotufo, R., Velastin, S.: On the efficacy of texture analysis for crowd monitoring. In: Proceedings of the International Symposium on Computer Graphics, Image Processing, and Vision, vol. 00, p. 354 (1998)
- Marana, A., Da Fontoura Costa, L., Lotufo, R., Velastin, S.: Estimating crowd density with Minkowski fractal dimension. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 1999. ICASSP’99. vol. 6, 3521–3524 (1999) · doi:10.1109/icassp.1999.757602
- Marana, A., Velastin, S., Costa, L., Lotufo, R.: Estimation of crowd density using image processing. In: IEE Colloquium on Image Processing for Security Applications (Digest No: 1997/074), p. 11 (1997) · doi:10.1049/ic:19970387
- Marana, A., Velastin, S., Costa, L., Lotufo, R.: Automatic estimation of crowd density using texture. Safety Sci. 28(3), 165–175 (1998) · DOI
- Marques, J., Jorge, P., Abrantes, A., Lemos, J.: Tracking groups of Pedestrians in video sequences. IEEE 2003 Conf. Comput. Vis. Pattern Recogn. Workshop 9, 101 (2003) · doi:10.1109/cvprw.2003.10103
- Mathes, T., Piater, J.: Robust non-rigid object tracking using point distribution models. Bri. Mach. Vis. Conf. 2 (2005) · DOI
- Maurin, B., Masoud, O., Papanikolopoulos, N.: Monitoring crowded traffic scenes. In: Proceedings of The IEEE 5th International Conference on Intelligent Transportation Systems, 2002. pp. 19–24 (2002)
- McKenna, S., Jabri, S., Duric, Z., Rosenfeld, A., Wechsler, H.: Tracking groups of people. Comput. Vis. Image Understanding 80(1), 42–56 (2000) · DOI · Paper page
- Mittal, A., Davis, L.: M 2 tracker: a multi-view approach to segmenting and tracking people in a cluttered scene. Int. J. Comput. Vis. 51(3), 189–203 (2003) · DOI · Paper page
- Musse, S., Thalmann, D.: A model of human crowd behavior: group inter-relationship and collision detection analysis. Proc. Workshop Comput. Anim. Simul. Eurograph. 97, 39–51 (1997)
- Okuma, K., Taleghani, A., de Freitas, N., Little, J., Lowe, D.: A boosted particle filter: Multitarget detection and tracking. Eur. Conf. Comput. Vis. 1, 28–39 (2004)
- Pan, X., Han, C., Dauber, K., Law, K.: Human and social behavior in computational modeling and analysis of egress. Automation in Construction 15(4), 448–461 (2006) · DOI · Paper page
- Polus, A., Schofer, J., Ushpiz, A.: Pedestrian Flow and Level of Service. J. Transportation Eng. 109(1), 46–56 (1983) · doi:10.1061/(asce)0733-947x(1983)109:1(46 · Paper page
- Rahmalan, H., Nixon, M., Carter, J.: On crowd density estimation for surveillance. The Institution of Engineering and Technology Conference on Crime and Security, pp. 540C–545C (2006) · DOI · Paper page
- Rasmussen, C., Hager, G.: Joint probabilistic techniques for tracking multi-part objects. Computer Vision and Pattern Recognition, 1998. In: Proceedings of 1998 IEEE Computer Society Conference, pp. 16–21 (1998)
- Reisman, P., Mano, O., Avidan, S., Shashua, A., Ltd, M., Jerusalem, I.: Crowd detection in video sequences. Intell. Vehicles Symp. IEEE, 66–71 (2004)
- R.R., C., Hughes, R.: Mathematical modelling of a mediaeval battle: the battle of agincourt. Math. Compute. Simul. 64(2), 259–269 (2004) · doi:10.1016/j.matcom.2003.09.019
- Shashua, A., Gdalyahu, Y., Hayun, G.: Pedestrian detection for driving assistance systems: single-frame classification and system level performance. Intelligent Vehicles Symposium, 2004 IEEE pp. 1–6 (2004)
- Sidenbladh, H., Wirkander, S.: Tracking random sets of vehicles in terrain. In: Proceedings of 2003 IEEE Workshop on Multi-Object Tracking vol. 9, 98 (2003) · DOI
- Siebel, N., Maybank, S.: Fusion of multiple tracking algorithms for robust people tracking. In: European Conference on Computer Vision, pp. 373–387 (2002) · doi:10.1007/3-540-47979-1_25
- Smith, K., Gatica-Perez, D., Odobez, J.: Using particles to track varying numbers of interacting people. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1–01, pp. 962–969 (2005)
- Spengler, M., Schiele, B.: Towards robust multi-cue integration for visual tracking. Mach. Vis. Appl. 14(1), 50–58 (2003) · DOI
- Sullivan, J., Carlsson, S.: Tracking and labelling of interacting multiple targets. In: European Conference on Computer Vision, LNCS, vol. 3953, pp. 619–632. Springer, Heidelberg (2006) · doi:10.1007/11744078_48
- Swets, D., Punch, B.: Genetic algorithms for object localization in a complex scene. In: IEEE International Conference on Image Processing, pp. 595–598 (1995)
- Velastin, S., Yin, J., Davies, A., Vicencio-Silva, M., Allsop, R., Penn, A.: Automated measurement of crowd density and motion using imageprocessing. Road traffic monitoring and control, 1994. In: Seventh International Conference, pp. 127–132 (1994) · doi:10.1049/cp:19940440
- Vu, V., Bremond, F., Thonnat, M.: Human behaviour visualisation and simulation for automatic video understanding. In: Proceedings of the 10th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG–2002), Plzen–Bory, Czech Republic, pp. 485–492 (2002)
- Wu, B., Nevatia, R.: Detection of Multiple, Partially Occluded Humans in a Single Image by Bayesian Combination of Edgelet Part Detectors. In: Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, vol. 1, 90–97 (2005)
- Wu, B., Nevatia, R.: Tracking of multiple, partially occluded humans based on static body part detection. In: CVPR ’06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 951–958 (2006)
- Yang, D., Gonzalez-Banos, H., Guibas, L.: Counting people in crowds with a real-time network of simple image sensors. In: Proceedings of Ninth IEEE International Conference on Computer Vision, 2003, pp. 122–129 (2003) · DOI · Paper page
- Yin, J., Velastin, S., Davies, A.: Image Processing Techniques for Crowd Density Estimation Using a Reference Image. Proc. 2nd Asia-Pacific Conf. Comput. Vis. 3, 6–10 (1995)
- Zhan, B., Remagnino, P., Velastin, S.: Analysing Crowd Intelligence. Second AIxIA Workshop on Ambient Intelligence (2005) · doi:10.1007/0-387-22991-4_1
- Zhan, B., Remagnino, P., Velastin, S.: Mining paths of complex crowd scenes. Lecture Notes in Computer Science pp. 126–133 (2005). ISBN/ISSN 3-540-30750-8 · doi:10.1007/11595755_16
- Zhan, B., Remagnino, P., Velastin, S.: Visual analysis of crowded pedestrain scenes. XLIII Congresso Annuale AICA, pp. 549–555 (2005)
- Zhao, T., Nevatia, R.: Tracking multiple humans in complex situations. Pattern Anal. Mach. Intell. IEEE Trans. 26(9), 1208–1221 (2004) · DOI · Paper page
- Zhao, T., Nevatia, R.: Tracking multiple humans in crowded environment. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition vol. 2, II–406–II–413 (2004)
Cited by
Scale Aggregation Network for Accurate and Efficient Crowd Counting
Cao, X., Wang, Z., Zhao, Y., & Su, F. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01228-1_45 · Cited by 733
A survey of appearance models in visual object tracking
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508039 · Cited by 705
Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies
Sadeghian, A., Alahi, A., & Savarese, S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.41 · Cited by 553
A survey of video datasets for human action and activity recognition
Chaquet, J. M., Carmona, E. J., & Fernández-Caballero, A. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.01.013 · Cited by 455
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
- Zhan, B., Monekosso, D. N., Remagnino, P., Velastin, S. A., & Xu, L.-Q.
- Venue
- Machine Vision and Applications
- Published
- 2008-04-10
- Volume
- 19
- Issue
- 5-6
- Pages
- 345-357
- ISSN
- 0932-8092 (ISSN-L)
- 1432-1769
- OpenAlex
- W2065994824
- Cited by
- 541
- References
- 1
- FWCI
- 31.02
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
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: Crowd analysis: a survey. https://app.cochat.ai/papers/10.1007/s00138-008-0132-4
- The paper
- Zhan, B., Monekosso, D. N., Remagnino, P., Velastin, S. A., & Xu, L.-Q. (2008). Crowd analysis: a survey. Machine Vision and Applications. https://doi.org/10.1007/s00138-008-0132-4
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.