Where are you going? An agent inclusive approach for path predictions in crowd

Yuke Li; Weiming Shen

doi:10.1117/1.JEI.26.4.043020

21 August 2017 Where are you going? An agent inclusive approach for path predictions in crowd

Yuke Li, Weiming Shen

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 4, 043020 (August 2017). https://doi.org/10.1117/1.JEI.26.4.043020

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.

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Yuke Li and Weiming Shen "Where are you going? An agent inclusive approach for path predictions in crowd," Journal of Electronic Imaging 26(4), 043020 (21 August 2017). https://doi.org/10.1117/1.JEI.26.4.043020

Received: 21 February 2017; Accepted: 26 July 2017; Published: 21 August 2017

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KEYWORDS

General packet radio service

Motion models

Feature extraction

Visual process modeling

Computer programming

Data modeling

Process modeling

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