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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sochman, J. Hogg, D.C. |
| Copyright Year | 2011 |
| Description | Author affiliation: CMP, Dep. of Cyber., FEE, CTU, Czech (Sochman, J.) || School of Computing, University of Leeds, UK (Hogg, D.C.) |
| Abstract | Social groups based on friendship or family relations are very common phenomena in human crowds and a valuable cue for a crowd activity recognition system. In this paper we present an algorithm for automatic on-line inference of social groups from observed trajectories of individual people. The method is based on the Social Force Model (SFM) - widely used in crowd simulation applications - which specifies several attractive and repulsive forces influencing each individual relative to the other pedestrians and their environment. The main contribution of the paper is an algorithm for inference of the social groups (parameters of the SFM) based on analysis of the observed trajectories through attractive or repulsive forces which could lead to such behaviour. The proposed SFM-based method shows its clear advantage especially in more crowded scenarios where other state-of-the-art methods fail. The applicability of the algorithm is illustrated on an abandoned bag scenario. |
| Starting Page | 830 |
| Ending Page | 837 |
| File Size | 6242238 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467300629 |
| e-ISBN | 9781467300636 |
| DOI | 10.1109/ICCVW.2011.6130338 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-11-06 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computational modeling Force Clustering algorithms Prediction algorithms Inference algorithms Trajectory Mathematical model |
| Content Type | Text |
| Resource Type | Article |
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