The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery
Open accessAbstract
This paper presents a robust occupancy analysis system for thermal imaging. Reliable detection of people is very hard in crowded scenes, due to occlusions and segmentation problems. We therefore propose a framework that optimises the occupancy analysis over long periods by including information on the transition in occupancy, when people enter or leave the monitored area. In stable periods, with no activity close to the borders, people are detected and counted which contributes to a weighted histogram. When activity close to the border is detected, local tracking is applied in order to identify a crossing. After a full sequence, the number of people during all periods are estimated using a probabilistic graph search optimisation. The system is tested on a total of 51,000 frames, captured in sports arenas. The mean error for a 30-minute period containing 3-13 people is 4.44 %, which is a half of the error percentage optained by detection only, and better than the results of comparable work. The framework is also tested on a public available dataset from an outdoor scene, which proves the generality of the method.
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Cites
A new method for gray-level picture thresholding using the entropy of the histogram
Kapur, J. N., Sahoo, P. K., & Wong, A. K. C. (1985) · Computer Vision, Graphics, and Image Processing · doi:10.1016/0734-189x(85)90125-2 · Cited by 3,658
Code/Space: Software and Everyday Life
Kitchin, R., & Dodge, M. (2011) · doi:10.7551/mitpress/9780262042482.001.0001 · Cited by 964
Physics for Scientists and Engineers with Modern Physics
Serway, R. A., & Kirkpatrick, L. D. (1988) · The Physics Teacher · doi:10.1119/1.2342517 · Cited by 902
Multicamera People Tracking with a Probabilistic Occupancy Map
Fleuret, F., Berclaz, J., Lengagne, R., & Fua, P. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1174 · Cited by 821
Code/space: software and everyday life
(2011) · Choice Reviews Online · doi:10.5860/choice.49-2108 · Cited by 570
Background-subtraction using contour-based fusion of thermal and visible imagery
Davis, J. W., & Sharma, V. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.06.010 · Cited by 418
Counting Crowded Moving Objects
Rabaud, V., & Belongie, S. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.92 · Cited by 410
Pedestrian Detection and Tracking With Night Vision
Xu, F., Liu, X., & Fujimura, K. (2005) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2004.838222 · Cited by 386
A Two-Stage Template Approach to Person Detection in Thermal Imagery
Davis, J. W., & Keck, M. A. (2005) · 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1 · doi:10.1109/acvmot.2005.14 · Cited by 320
Pedestrian Detection using Infrared images and Histograms of Oriented Gradients
Suard, F., Rakotomamonjy, A., Bensrhair, A., & Broggi, A. (2006) · 2006 IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2006.1689629 · Cited by 262
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Yuan, L., Chen, D., Chen, Y.-L., Codella, N., Dai, X., Gao, J., Hu, H., Huang, X., Li, B., Li, C., et al. (2021) · arXiv · doi:10.48550/arxiv.2111.11432 · Cited by 389
Metadata
- Authors
- Gade, R., Jorgensen, A., & Moeslund, T. B.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 3698-3705
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2170413022
- Cited by
- 437
- References
- 40
- FWCI
- 39.25
- Type
- conference paper
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
