Crowd Counting Using Group Tracking and Local Features

@article{Ryan2010CrowdCU,
  title={Crowd Counting Using Group Tracking and Local Features},
  author={David Ryan and Simon Denman and Clinton Fookes and Sridha Sridharan},
  journal={2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance},
  year={2010},
  pages={218-224}
}
In public venues, crowd size is a key indicator of crowdsafety and stability. In this paper we propose a crowd count-ing algorithm that uses tracking and local features to countthe number of people in each group as represented by a fore-ground blob segment, so that the total crowd estimate is thesum of the group sizes. Tracking is employed to improve therobustness of the estimate, by analysing the history of eachgroup, including splitting and merging events. A simpli-fied ground truth… CONTINUE READING
Highly Cited
This paper has 26 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Explore Further: Topics Discussed in This Paper

Citations

Publications citing this paper.
Showing 1-10 of 17 extracted citations

A scalable and privacy preserving approach for counting pedestrians in urban environment

2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) • 2015
View 6 Excerpts
Highly Influenced

Detecting Coherent Groups in Crowd Scenes by Multiview Clustering.

IEEE transactions on pattern analysis and machine intelligence • 2018
View 1 Excerpt

Filters for Wi-Fi Generated Crowd Movement Data

2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC) • 2015
View 1 Excerpt

Video-based crowd counting with information entropy

The 27th Chinese Control and Decision Conference (2015 CCDC) • 2015
View 2 Excerpts

Spatiotemporal Group Context for Pedestrian Counting

IEEE Transactions on Circuits and Systems for Video Technology • 2014
View 2 Excerpts

References

Publications referenced by this paper.
Showing 1-10 of 19 references

Privacy preserving crowd monitoring: Counting people without people models or tracking

2008 IEEE Conference on Computer Vision and Pattern Recognition • 2008
View 8 Excerpts
Highly Influenced

A Viewpoint Invariant Approach for Crowd Counting

18th International Conference on Pattern Recognition (ICPR'06) • 2006
View 5 Excerpts
Highly Influenced

Crowd Counting Using Multiple Local Features

2009 Digital Image Computing: Techniques and Applications • 2009
View 10 Excerpts

Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2008
View 1 Excerpt

A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis

2007 IEEE Conference on Computer Vision and Pattern Recognition • 2007

Similar Papers

Loading similar papers…