The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Crowd Counting Using Multiple Local Features
Open accessAbstract
In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.
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Cites
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
A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis
Ali, S., & Shah, M. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.382977 · Cited by 590
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
Chan, A. B., & Vasconcelos, N. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70738 · Cited by 459
Crowd monitoring using image processing
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Estimation of number of people in crowded scenes using perspective transformation
Lin, S.-F., Chen, J.-Y., & Chao, H.-X. (2001) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.983420 · Cited by 330
Bayesian human segmentation in crowded situations
Zhao, T., & Nevatia, R. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211503 · Cited by 294
A MRF-based approach for real-time subway monitoring
Paragios, N., & Ramesh, V. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990644 · Cited by 252
A Viewpoint Invariant Approach for Crowd Counting
Kong, D., Gray, D., & Tao, H. (2006) · 18th International Conference on Pattern Recognition (ICPR'06) · doi:10.1109/icpr.2006.197 · Cited by 251
A novel method for tracking and counting pedestrians in real-time using a single camera
Masoud, O., & Papanikolopoulos, N. P. (2001) · IEEE Transactions on Vehicular Technology · doi:10.1109/25.950328 · Cited by 227
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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
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
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
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
Metadata
- Authors
- Ryan, D., Denman, S., Fookes, C., & Sridharan, S.
- Venue
- 2009 Digital Image Computing: Techniques and Applications
- Publisher
- IEEE
- Published
- 2009
- Pages
- 81-88
- OpenAlex
- W2155916750
- Cited by
- 309
- References
- 14
- FWCI
- 4.63
- Type
- conference paper
- Language
- en
- Open access
- green
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