DocumentCode :
3681586
Title :
Pedestrian Intention and Pose Prediction through Dynamical Models and Behaviour Classification
Author :
R. Quintero;I. Parra;D. F. Llorca;M. A. Sotelo
Author_Institution :
Comput. Eng. Dept., Univ. of Alcala, Alcala de Henares, Spain
fYear :
2015
Firstpage :
83
Lastpage :
88
Abstract :
Pedestrian protection systems are being included by many automobile manufacturers in their commercial vehicles. However, improving the accuracy of these systems is imperative since the difference between an effective and a non-effective intervention can depend only on a few centimeters or on a fraction of a second. In this paper, we describe a method to carry out the prediction of pedestrian locations and pose and to classify intentions up to 1 s ahead in time applying Balanced Gaussian Process Dynamical Models (B-GPDM) and naïve-Bayes classifiers. These classifiers are combined in order to increase the action classification precision. The system provides accurate path predictions with mean errors of 24.4 cm, for walking trajectories, 26.67 cm, for stopping trajectories and 37.36 cm for starting trajectories, at a time horizon of 1 second.
Keywords :
"Legged locomotion","Joints","Trajectory","Kernel","Predictive models","Computational modeling","Yttrium"
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on
ISSN :
2153-0009
Electronic_ISBN :
2153-0017
Type :
conf
DOI :
10.1109/ITSC.2015.22
Filename :
7313114
Link To Document :
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