Comparative analysis of human motion trajectory prediction using minimum variance curvature

Authors:
Gonzalo Ferrer;Alberto Sanfeliu
Affiliations:
Institut de Róobitica i Informática Industrial, Barcelona, Spain;Institut de Róobitica i Informática Industrial, Barcelona, Spain
Venue:
Proceedings of the 6th international conference on Human-robot interaction
Year:
2011

Citing 2
Cited 0

Growing Hidden Markov Models: An Incremental Tool for Learning and Predicting Human and Vehicle Motion

International Journal of Robotics Research
An Optimality Principle Governing Human Walking

IEEE Transactions on Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The prediction of human motion intentionality is a key issue towards intelligent human robot interaction and robot navigation. In this work we present a comparative study of several prediction functions that are based on the minimum curvature variance from the current position to all the potential destination points, that means, the points that are relevant for people motion intentionality. The proposed predictor computes, at each interval of time, the trajectory from the present to the destination positions, and makes a prediction of the human motion at each interval of time using only the criterion of minimum curvature variation. The method has been validated in the Edinburgh Informatics Forum Pedestrian database.