The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
Abstract
Our work proposes a novel deep learning framework for estimating crowd density from static images of highly dense crowds. We use a combination of deep and shallow, fully convolutional networks to predict the density map for a given crowd image. Such a combination is used for effectively capturing both the high-level semantic information (face/body detectors) and the low-level features (blob detectors), that are necessary for crowd counting under large scale variations. As most crowd datasets have limited training samples (<100 images) and deep learning based approaches require large amounts of training data, we perform multi-scale data augmentation. Augmenting the training samples in such a manner helps in guiding the CNN to learn scale invariant representations. Our method is tested on the challenging UCF_CC_50 dataset, and shown to outperform the state of the art methods.
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
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
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213
Advances in Neural Information Processing Systems 21
Griffiths, T. L., Lucas, C. G., Williams, J. J., & Kalish, M. L. (2009) · neural information processing systems · Cited by 1,854
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
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
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
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
Papandreou, G., Chen, L.-C., Murphy, K. P., & Yuille, A. L. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.203 · Cited by 1,005
Cited by
FCOS: Fully Convolutional One-Stage Object Detection
Tian, Z., Shen, C., Chen, H., & He, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00972 · Cited by 7,013
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
CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting
Sindagi, V. A., & Patel, V. M. (2017) · 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078491 · Cited by 536
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
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
Liu, X., van de Weijer, J., & Bagdanov, A. D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00799 · Cited by 308
Metadata
- Authors
- Boominathan, L., Kruthiventi, S. S. S., & Babu, R. V.
- Venue
- Proceedings of the 24th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2016-10
- Pages
- 640-644
- OpenAlex
- W2517615595
- Cited by
- 503
- References
- 19
- FWCI
- 17.50
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Boominathan, L., Kruthiventi, S. S. S., & Babu, R. V. (2016). CrowdNet: A Deep Convolutional Network for Dense Crowd Counting. Proceedings of the 24th ACM international conference on Multimedia. https://doi.org/10.1145/2964284.2967300
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