This study focuses on joint motion patterns of humans that move together with other humans or objects. Since this scope embraces 'group motion', which relates only humans, and expands its extent of interactions accounting for various auxiliary instruments such as walking aids or pushcarts, we term this collective motion pattern as 'coherent' motion. Coherence is proposed to be characterized by the distance between the moving parties, the scalar product of their velocities and the scalar product of the velocity vector and the displacement vector. The contribution of this study lies in the formulation of coherence in terms of the listed features through explicit mathematical models. The models are developed in accordance with a large database recorded in an uncontrolled environment involving a total of more than 500 mobile entities. The performance of the proposed models is evaluated qualitatively by comparing them to the empirical data and quantitatively by employing log-likelihoods. Comparison to an earlier work indicates that the proposed models improve the identification of coherence quality significantly well.

Modeling Indicators of Coherent Motion

Yucel, Z;
2012-01-01

Abstract

This study focuses on joint motion patterns of humans that move together with other humans or objects. Since this scope embraces 'group motion', which relates only humans, and expands its extent of interactions accounting for various auxiliary instruments such as walking aids or pushcarts, we term this collective motion pattern as 'coherent' motion. Coherence is proposed to be characterized by the distance between the moving parties, the scalar product of their velocities and the scalar product of the velocity vector and the displacement vector. The contribution of this study lies in the formulation of coherence in terms of the listed features through explicit mathematical models. The models are developed in accordance with a large database recorded in an uncontrolled environment involving a total of more than 500 mobile entities. The performance of the proposed models is evaluated qualitatively by comparing them to the empirical data and quantitatively by employing log-likelihoods. Comparison to an earlier work indicates that the proposed models improve the identification of coherence quality significantly well.
2012
Proc. IEEE/RSJ International Conf. on Intelligent Robots and Systems (IROS 2012)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5079364
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