The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Density-aware person detection and tracking in crowds
Open accessAbstract
We address the problem of person detection and tracking in crowded video scenes. While the detection of individual objects has been improved significantly over the recent years, crowd scenes remain particularly challenging for the detection and tracking tasks due to heavy occlusions, high person densities and significant variation in people's appearance. To address these challenges, we propose to leverage information on the global structure of the scene and to resolve all detections jointly. In particular, we explore constraints imposed by the crowd density and formulate person detection as the optimization of a joint energy function combining crowd density estimation and the localization of individual people. We demonstrate how the optimization of such an energy function significantly improves person detection and tracking in crowds. We validate our approach on a challenging video dataset of crowded scenes.
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Cites
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
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
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724 · cited 1×
“For each crowd video sequence, we obtain point tracks using the Kanade-Lucas-Tomasi tracker [28].”
MethodYou'll never walk alone: Modeling social behavior for multi-target tracking
Pellegrini, S., Ess, A., Schindler, K., & van Gool, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459260 · Cited by 1,683 · cited 2×
“In the area of tracking, [4, 16, 22, 23] demonstrate how the tracking accuracy can be improved by the joint modeling of motion of multiple people in the scene.”
Background
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
Learning Non-maximum Suppression
Hosang, J., Benenson, R., & Schiele, B. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.685 · Cited by 736
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
Continuous Energy Minimization for Multitarget Tracking
Milan, A., Roth, S., & Schindler, K. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.103 · Cited by 565
Metadata
- Authors
- Rodriguez, M., Laptev, I., Sivic, J., & Audibert, J.-Y.
- Venue
- 2011 International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 2423-2430
- OpenAlex
- W2147221461
- Cited by
- 341
- References
- 4
- FWCI
- 13.87
- Type
- conference paper
- Language
- en
- Open access
- green
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- Rodriguez, M., Laptev, I., Sivic, J., & Audibert, J.-Y. (2011). Density-aware person detection and tracking in crowds. 2011 International Conference on Computer Vision. https://doi.org/10.1109/iccv.2011.6126526
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