The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Deep People Counting in Extremely Dense Crowds
Abstract
People counting in extremely dense crowds is an important step for video surveillance and anomaly warning. The problem becomes especially more challenging due to the lack of training samples, severe occlusions, cluttered scenes and variation of perspective. Existing methods either resort to auxiliary human and face detectors or surrogate by estimating the density of crowds. Most of them rely on hand-crafted features, such as SIFT, HOG etc, and thus are prone to fail when density grows or the training sample is scarce. In this paper we propose an end-to-end deep convolutional neural networks (CNN) regression model for counting people of images in extremely dense crowds. Our method has following characteristics. Firstly, it is a deep model built on CNN to automatically learn effective features for counting. Besides, to weaken influence of background like buildings and trees, we purposely enrich the training data with expanded negative samples whose ground truth counting is set as zero. With these negative samples, the robustness can be enhanced. Extensive experimental results show that our method achieves superior performance than the state-of-the-arts in term of the mean and variance of absolute difference.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
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
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
The pyramid match kernel: discriminative classification with sets of image features
Grauman, K., & Darrell, T. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.239 · Cited by 1,481
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
Marked point processes for crowd counting
Ge, W., & Collins, R. T. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206621 · Cited by 253
Matching-CNN meets KNN: Quasi-parametric human parsing
Liu, S., Liang, X., Liu, L., Shen, X., Yang, J., Xu, C., Lin, L., Cao, X., & Yan, S. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298748 · Cited by 142
Self-citationFully Convolutional Neural Networks for Crowd SegmentationPreprint
Kang, K., & Wang, X. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.4464 · Cited by 131
Crowd Detection from Still Images
Arandjelović, O. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.53 · Cited by 51
Cited by
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
Idrees, H., Tayyab, M., Athrey, K., Zhang, D., Al-Maadeed, S., Rajpoot, N., & Shah, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_33 · Cited by 851
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
Self-citationCNN-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
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
Boominathan, L., Kruthiventi, S. S. S., & Babu, R. V. (2016) · Proceedings of the 24th ACM international conference on Multimedia · doi:10.1145/2964284.2967300 · Cited by 503
A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning
Mundhenk, T. N., Konjevod, G., Sakla, W. A., & Boakye, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_48 · Cited by 386
Metadata
- Authors
- Wang, C., Zhang, H., Yang, L., Liu, S., & Cao, X.
- Venue
- Proceedings of the 23rd ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2015-10-13
- Pages
- 1299-1302
- OpenAlex
- W1978232622
- Cited by
- 386
- References
- 10
- FWCI
- 8.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, C., Zhang, H., Yang, L., Liu, S., & Cao, X. (2015). Deep People Counting in Extremely Dense Crowds. Proceedings of the 23rd ACM international conference on Multimedia. https://doi.org/10.1145/2733373.2806337
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