The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation
Abstract
This paper presents a patch-based approach for crowd density estimation in public scenes. We formulate the problem of estimating density in a structured learning framework applied to random decision forests. Our approach learns the mapping between patch features and relative locations of all objects inside each patch, which contribute to generate the patch density map through Gaussian kernel density estimation. We build the forest in a coarse-to-fine manner with two split node layers, and further propose a crowdedness prior and an effective forest reduction method to improve the estimation accuracy and speed. Moreover, we introduce a semi-automatic training method to learn the estimator for a specific scene. We achieved state-of-the-art results on the public Mall dataset and UCSD dataset, and also proposed two potential applications in traffic counts and scene understanding with promising results.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Classification and Regression by randomForest
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Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703
Shape Quantization and Recognition with Randomized Trees
Amit, Y., & Geman, D. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1545 · Cited by 1,275
Privacy preserving crowd monitoring: Counting people without people models or tracking
Chan, A. B., Liang, Z.-S. J., & Vasconcelos, N. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587569 · Cited by 1,172
Learning To Count Objects in Images
Lempitsky, V., & Zisserman, A. (2010) · Oxford University Research Archive (ORA) (University of Oxford) · Cited by 1,063
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
Ten Years of Pedestrian Detection, What Have We Learned?
Benenson, R., Omran, M., Hosang, J., & Schiele, B. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16181-5_47 · Cited by 558
Class-Specific Hough Forests for Object Detection
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Cited by
Towards Perspective-Free Object Counting with Deep Learning
Oñoro-Rubio, D., & López-Sastre, R. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46478-7_38 · Cited by 641
Attention Scaling for Crowd Counting
Jiang, X., Zhang, L., Xu, M., Zhang, T., Lv, P., Zhou, B., Yang, X., & Pang, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00476 · Cited by 325
Survey of Non-Image-Based Approaches for Counting People
Kouyoumdjieva, S. T., Danielis, P., & Karlsson, G. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2902824 · Cited by 61
DeepSetNet: Predicting Sets with Deep Neural Networks
Rezatofighi, S. H., B. G., V. K., Milan, A., Abbasnejad, E., Dick, A., & Reid, I. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.561 · Cited by 45
Metadata
- Authors
- Pham, V.-Q., Kozakaya, T., Yamaguchi, O., & Okada, R.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 3253-3261
- OpenAlex
- W2207893099
- Cited by
- 363
- References
- 27
- FWCI
- 7.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Pham, V.-Q., Kozakaya, T., Yamaguchi, O., & Okada, R. (2015). COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.372
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