Title

Learning Pedestrian Dynamics From The Real World

Abstract

In this paper we describe a method to learn parameters which govern pedestrian motion by observing video data. Our learning framework is based on variational mode learning and allows us to efficiently optimize a continuous pedestrian cost model. We show that this model can be trained on automatic tracking results, and provides realistic and accurate pedestrian motions. ©2009 IEEE.

Publication Date

12-1-2009

Publication Title

Proceedings of the IEEE International Conference on Computer Vision

Number of Pages

381-388

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCV.2009.5459224

Socpus ID

77953186524 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77953186524

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