Paper Info
Title
Road direction detection based on vanishing-point tracking.
Abstract
We present a novel approach for vision-based road direction detection for autonomous Unmanned Ground Vehicles (UGVs). The proposed method utilizes only monocular vision information similar to human perception to detect road directions with respect to the vehicle. The algorithm searches for a global feature of the roads due to perspective projection (so-called vanishing point) to distinguish road directions. The proposed approach consists of two stages. The first stage estimates the vanishing-point locations from single frames. The second stage uses a Rao-Blackwellised particle filter to track initial vanishing-point estimations over a sequence of images in order to provide more robust estimation. Simultaneously, the direction of the road ahead of the vehicle is predicted, which is prerequisite information for vehicle steering and path planning. The proposed approach assumes minimum prior knowledge about the environment and can cope with complex situations such as ground cover variations, different illuminations, and cast shadows. Its performance is evaluated on video sequences taken during test run of the DARPA Grand Challenge.
Year
DOI
Venue
2012
10.1109/IROS.2012.6386089
IROS
Keywords
Field
DocType
image sequences,mobile robots,object detection,object tracking,particle filtering (numerical methods),path planning,remotely operated vehicles,road vehicles,robot vision,robust control,video signal processing,Rao-Blackwellised particle filter,UGV,autonomous unmanned ground vehicle,cast shadow,ground cover variation,human perception,illumination,image sequence,monocular vision information,path planning,road global feature,robust estimation,vanishing-point estimation,vanishing-point location,vanishing-point tracking,vehicle steering,video sequence,vision-based road direction detection
Motion planning,Monocular vision,Object detection,Computer vision,Remotely operated underwater vehicle,Computer science,Particle filter,Perspective (graphical),Video tracking,Artificial intelligence,Vanishing point
Conference
ISSN
Citations 
PageRank 
2153-0858
11
0.64
References 
Authors
16
2
Name
Order
Citations
PageRank
Peyman Moghadam116512.92
Jun Feng Dong2151.05