The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Feature Mining for Localised Crowd Counting
Abstract
This paper presents a multi-output regression model for crowd counting in public scenes. Existing counting by regression methods either learn a single model for global counting, or train a large number of separate regressors for localised density estimation. In contrast, our single regression model based approach is able to estimate people count in spatially localised regions and is more scalable without the need for training a large number of regressors proportional to the number of local regions. In particular, the proposed model automatically learns the functional mapping between interdependent low-level features and multi-dimensional structured outputs. The model is able to discover the inherent importance of different features for people counting at different spatial locations. Extensive evaluations on an existing crowd analysis benchmark dataset and a new more challenging dataset demonstrate the effectiveness of our approach. 1
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Citations
Cites
Learning to Localize Objects with Structured Output Regression
Blaschko, M. B., & Lampert, C. H. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_2 · Cited by 324
Cited by
Cross-scene crowd counting via deep convolutional neural networks
Zhang, C., Li, H., Wang, X., & Yang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298684 · Cited by 1,121
A comprehensive review on ensemble deep learning: Opportunities and challenges
Mohammed, A., & Kora, R. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.01.014 · Cited by 1,127
Learning From Synthetic Data for Crowd Counting in the Wild
Wang, Q., Gao, J., Lin, W., & Yuan, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00839 · Cited by 654
Drone-Based Object Counting by Spatially Regularized Regional Proposal Network
Hsieh, M.-R., Lin, Y.-L., & Hsu, W. H. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.446 · Cited by 547
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
Wang, Q., Gao, J., Lin, W., & Li, X. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3013269 · Cited by 510
Metadata
- Authors
- Chen, K., Loy, C. C., Gong, S., & Xiang, T.
- Venue
- Procedings of the British Machine Vision Conference 2012
- Publisher
- British Machine Vision Association
- Published
- 2012
- Pages
- 21.1-21.11
- OpenAlex
- W1976959044
- Cited by
- 666
- References
- 1
- FWCI
- 5.70
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, K., Loy, C. C., Gong, S., & Xiang, T. (2012). Feature Mining for Localised Crowd Counting. Procedings of the British Machine Vision Conference 2012. https://doi.org/10.5244/c.26.21
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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