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2008 IEEE Workshop on Applications of Computer Vision
Recovering Social Networks From Massive Track Datasets
Copper Mountain, CO, USA
January 07-January 09
ISBN: 978-1-4244-1913-5
| ASCII Text | x | ||
| Christopher I. Connolly, J. Brian Burns, Hung H. Bui, "Recovering Social Networks From Massive Track Datasets," Applications of Computer Vision, IEEE Workshop on, pp. 1-8, 2008 IEEE Workshop on Applications of Computer Vision, 2008. | |||
| BibTex | x | ||
| @article{ 10.1109/WACV.2008.4544042, author = {Christopher I. Connolly and J. Brian Burns and Hung H. Bui}, title = {Recovering Social Networks From Massive Track Datasets}, journal ={Applications of Computer Vision, IEEE Workshop on}, volume = {0}, year = {2008}, isbn = {978-1-4244-1913-5}, pages = {1-8}, doi = {http://doi.ieeecomputersociety.org/10.1109/WACV.2008.4544042}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Applications of Computer Vision, IEEE Workshop on TI - Recovering Social Networks From Massive Track Datasets SN - 978-1-4244-1913-5 SP1 EP8 A1 - Christopher I. Connolly, A1 - J. Brian Burns, A1 - Hung H. Bui, PY - 2008 VL - 0 JA - Applications of Computer Vision, IEEE Workshop on ER - | |||
Analysis of massive track datasets is a challenging problem, especially when examining n-way relations inherent in social networks. In this paper, we use the Mitsubishi track database to examine the usefulness of three types of interaction features observable in tracklet networks. We explore ways in which social network information can be extracted and visualized using a statistical sampling of these features from a very large track dataset, with very little ground truth or outside knowledge. Special attention is given to methods that are likely to scale well beyond the size of the Mitsubishi dataset.
Citation:
Christopher I. Connolly, J. Brian Burns, Hung H. Bui, "Recovering Social Networks From Massive Track Datasets," wacv, pp.1-8, 2008 IEEE Workshop on Applications of Computer Vision, 2008
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