The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Associating Groups of People
Abstract
In a crowded public space, people often walk in groups, either with people they know or strangers. Associating a group of people over space and time can assist understanding individual’s behaviours as it provides vital visual context for matching individuals within the group. Seemingly an ‘easier’ task compared with person matching given more and richer visual content, this problem is in fact very challenging because a group of people can be highly non-rigid with changing relative position of people within the group and severe self-occlusions. In this paper, for the first time, the problem of matching/associating groups of people over large space and time captured in multiple non-overlapping camera views is addressed. Specifically, a novel people group representation and a group matching algorithm are proposed. The former addresses changes in the relative positions of people in a group and the latter deals with variations in illumination and viewpoint across camera views. In addition, we demonstrate a notable enhancement on individual person matching by utilising the group description as visual context. Our methods are validated using the 2008 i-LIDS Multiple-Camera Tracking Scenario (MCTS) dataset on multiple camera views from a busy airport arrival hall.
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Cites
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
Cited by
MARS: A Video Benchmark for Large-Scale Person Re-Identification
Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., & Tian, Q. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_52 · Cited by 965
Person Re-identification by Descriptive and Discriminative Classification
Hirzer, M., Beleznai, C., Roth, P. M., & Bischof, H. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-21227-7_9 · Cited by 961
Custom Pictorial Structures for Re-identification
Cheng, D. S., Cristani, M., Stoppa, M., Bazzani, L., & Murino, V. (2011) · Procedings of the British Machine Vision Conference 2011 · doi:10.5244/c.25.68 · Cited by 616
Style Normalization and Restitution for Generalizable Person Re-Identification
Jin, X., Lan, C., Zeng, W., Chen, Z., & Zhang, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00321 · Cited by 426
A survey of approaches and trends in person re-identification
Bedagkar-Gala, A., & Shah, S. K. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.02.001 · Cited by 420
Metadata
- Authors
- Zheng, W.-S., Gong, S., & Xiang, T.
- Venue
- Procedings of the British Machine Vision Conference 2009
- Publisher
- British Machine Vision Association
- Published
- 2009
- Pages
- 23.1-23.11
- OpenAlex
- W2139763424
- Cited by
- 444
- References
- 2
- FWCI
- 14.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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