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Journal of Electronic Imaging

Where are you going? An agent inclusive approach for path predictions in crowd
Author(s): Yuke Li; Weiming Shen
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Paper Abstract

Pedestrian path forecasting is one of the recently emerging applications in visual crowd analysis and modeling. Moreover, of the attempts proposed to date, only a few have considered that the undergoing interaction among agents is a key factor in determining their walking trends in a given scene. To this end, we propose a simple yet efficient framework for pedestrian path prediction in crowded scenes. First, we extract motion features related to the target pedestrian and its nearby neighbors. Second, we adopt an autoencoder feature-learning model to further enhance the representation of the extracted features. Finally, we utilize a Gaussian process regression model to infer the potential future trajectories of the target pedestrians given their walking history in the scene. We performed experiments using a challenging dataset, and our method yielded promising results and outperformed traditional methods proposed in the literature.

Paper Details

Date Published: 21 August 2017
PDF: 6 pages
J. Electron. Imag. 26(4) 043020 doi: 10.1117/1.JEI.26.4.043020
Published in: Journal of Electronic Imaging Volume 26, Issue 4
Show Author Affiliations
Yuke Li, Wuhan Univ. (China)
Weiming Shen, Wuhan Univ. (China)

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