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.
"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
. Submission: Received: 21 February 2017; Accepted: 26 July 2017
Received: 21 February 2017; Accepted: 26 July 2017; Published: 21 August 2017