TY - GEN
T1 - Real-time automotive street-scene mapping through fusion of improved stereo depth and fast feature detection algorithms
AU - Javidnia, Hossein
AU - Corcoran, Peter
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/3/29
Y1 - 2017/3/29
N2 - The real-time tracking of street scenes as a vehicle is driving is a key enabling technology for autonomous vehicles. In this work we provide the basis for such a system through combining an improved advanced random walk with restart technique for stereo depth determination with fast, robust feature detection. The enables tracking and mapping of a wide range of scene structures which can be readily resolved into individual objects and scene elements. Thus it is practical to identify moving objects such as vehicles, pedestrians and fixed objects and structures such as buildings, trees and roadside kerb.
AB - The real-time tracking of street scenes as a vehicle is driving is a key enabling technology for autonomous vehicles. In this work we provide the basis for such a system through combining an improved advanced random walk with restart technique for stereo depth determination with fast, robust feature detection. The enables tracking and mapping of a wide range of scene structures which can be readily resolved into individual objects and scene elements. Thus it is practical to identify moving objects such as vehicles, pedestrians and fixed objects and structures such as buildings, trees and roadside kerb.
UR - https://www.scopus.com/pages/publications/85018349492
U2 - 10.1109/ICCE.2017.7889293
DO - 10.1109/ICCE.2017.7889293
M3 - Conference Publication
T3 - 2017 IEEE International Conference on Consumer Electronics, ICCE 2017
SP - 225
EP - 228
BT - 2017 IEEE International Conference on Consumer Electronics, ICCE 2017
A2 - Sanchez, Daniel Diaz
A2 - Lee, Jong-Hyouk
A2 - Pescador, Fernando
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Consumer Electronics, ICCE 2017
Y2 - 8 January 2017 through 10 January 2017
ER -