TY - GEN
T1 - Context-based pedestrian path prediction
AU - Kooij, Julian Francisco Pieter
AU - Schneider, Nicolas
AU - Flohr, Fabian
AU - Gavrila, Dariu M.
PY - 2014
Y1 - 2014
N2 - We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method.
AB - We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method.
KW - Dynamic Bayesian Network
KW - intelligent vehicles
KW - Linear Dynamical System
KW - path prediction
KW - situational awareness
KW - visual focus of attention
UR - http://www.scopus.com/inward/record.url?scp=84906351374&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10599-4_40
DO - 10.1007/978-3-319-10599-4_40
M3 - Conference contribution
AN - SCOPUS:84906351374
SN - 9783319105987
VL - 8694 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 618
EP - 633
BT - Computer Vision, ECCV 2014 - 13th European Conference, Proceedings
PB - Springer
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -