The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Cross-scene crowd counting via deep convolutional neural networks
Abstract
Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to fine-tune the trained CNN model for the target scene. A new dataset including 108 crowd scenes with nearly 200,000 head annotations is introduced to better evaluate the accuracy of cross-scene crowd counting methods. Extensive experiments on the proposed and another two existing datasets demonstrate the effectiveness and reliability of our approach.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
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DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
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Self-citationSIFT Flow: Dense Correspondence across Scenes and Its Applications
Liu, C., Yuen, J., & Torralba, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.147 · Cited by 1,582
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
Multi-source Multi-scale Counting in Extremely Dense Crowd Images
Idrees, H., Saleemi, I., Seibert, C., & Shah, M. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.329 · Cited by 1,031
Learning a Deep Compact Image Representation for Visual Tracking
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Feature Mining for Localised Crowd Counting
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Cited by
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719367115 · Cited by 1,230
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
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
Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., & Yu, N. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.518 · Cited by 406
Self-citation
Metadata
- Authors
- Zhang, C., Li, H., Wang, X., & Yang, X.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 833-841
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1910776219
- Cited by
- 1,121
- References
- 29
- FWCI
- 32.98
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, C., Li, H., Wang, X., & Yang, X. (2015). Cross-scene crowd counting via deep convolutional neural networks. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298684
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