Paper Info
Title
Combined head localization and head pose estimation for video-based advanced driver assistance systems
Abstract
This work presents a novel approach for pedestrian head localization and head pose estimation in single images. The presented method addresses an environment of low resolution gray-value images taken from a moving camera with large variations in illumination and object appearance. The proposed algorithms are based on normalized detection confidence values of separate, pose associated classifiers. Those classifiers are trained using a modified one vs. all framework that tolerates outliers appearing in continuous head pose classes. Experiments on a large set of real world data show very good head localization and head pose estimation results even on the smallest considered head size of 7×7 pixels. These results can be obtained in a probabilistic form, which make them of a great value for pedestrian path prediction and risk assessment systems within video-based driver assistance systems or many other applications.
Year
DOI
Venue
2011
10.1007/978-3-642-23123-0_6
DAGM-Symposium
Keywords
Field
DocType
continuous head,pedestrian head localization,estimation result,combined head localization,large set,head size,pedestrian path prediction,great value,video-based advanced driver assistance,large variation,low resolution gray-value image,good head localization
Computer vision,Normalization (statistics),Pattern recognition,Computer science,Advanced driver assistance systems,3D pose estimation,Outlier,Pose,Pixel,Artificial intelligence,Probabilistic logic,Articulated body pose estimation
Conference
Citations 
PageRank 
References 
8
0.56
17
Authors
4
Name
Order
Citations
PageRank
Andreas Schulz1281.96
Naser Damer211730.86
Mika Fischer31028.38
Rainer Stiefelhagen43512274.86