Paper Info
Title
Video-Based Pedestrian Head Pose Estimation For Risk Assessment
Abstract
Pedestrian detection and action recognition is a demanding field in video-based driver assistance systems. Future systems not only try to detect pedestrians but also aim to predict the pedestrian's intention in order to guaranty the best safety for him/her and other traffic participants. Our contribution to that tendency consists of a method to reliably estimate the pedestrians' head pose in low resolution video sequences taken from an on-board camera. Assuming a pre-detected pedestrian, the head pose is initialized using normalized confidence values from a set of head pose detectors. Integrating the head pose predictions over time using particle-filtering will further result in a higher robustness and efficiency. Experiments on public available datasets (CHIL/CLEAR2007, CAVIAR) and real world scenarios show a performance improvement compared to single image based approaches. The developed method can be integrated easily into an overall system and will serve for a better pedestrian path prediction and intension estimation within risk assessment.
Year
DOI
Venue
2012
10.1109/ITSC.2012.6338829
2012 15TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
Keywords
Field
DocType
detectors,risk management,head,robustness,pose estimation,estimation,risk assessment
Computer vision,Pedestrian,Normalization (statistics),Simulation,Advanced driver assistance systems,Robustness (computer science),Pose,Risk management,Artificial intelligence,Engineering,Pedestrian detection,Performance improvement
Conference
ISSN
Citations 
PageRank 
2153-0009
11
0.58
References 
Authors
5
2
Name
Order
Citations
PageRank
Andreas Schulz1281.96
Rainer Stiefelhagen23512274.86