The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Marked point processes for crowd counting
Abstract
A Bayesian marked point process (MPP) model is developed to detect and count people in crowded scenes. The model couples a spatial stochastic process governing number and placement of individuals with a conditional mark process for selecting body shape. We automatically learn the mark (shape) process from training video by estimating a mixture of Bernoulli shape prototypes along with an extrinsic shape distribution describing the orientation and scaling of these shapes for any given image location. The reversible jump Markov Chain Monte Carlo framework is used to efficiently search for the maximum a posteriori configuration of shapes, leading to an estimate of the count, location and pose of each person in the scene. Quantitative results of crowd counting are presented for two publicly available datasets with known ground truth.
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Cites
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
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
Feature Mining for Localised Crowd Counting
Chen, K., Loy, C. C., Gong, S., & Xiang, T. (2012) · Procedings of the British Machine Vision Conference 2012 · doi:10.5244/c.26.21 · Cited by 666
Deep People Counting in Extremely Dense Crowds
Wang, C., Zhang, H., Yang, L., Liu, S., & Cao, X. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2806337 · Cited by 386
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation
Pham, V.-Q., Kozakaya, T., Yamaguchi, O., & Okada, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.372 · Cited by 363
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
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Metadata
- Authors
- Ge, W., & Collins, R. T.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 2913-2920
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2122243179
- Cited by
- 253
- References
- 1
- FWCI
- 9.27
- Type
- conference paper
- Language
- en
- Open access
- closed
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