2017 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)

Peter Corcoran

2017 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)

Electrical & Electronic Engineering

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

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.

Original language	English (Ireland)
Title of host publication	Real-Time Automotive Street-Scene Mapping Through Fusion of Improved Stereo Depth and Fast Feature Detection Algorithms
Publication status	Published - 1 Jan 2017

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Javidnia, H,Corcoran, P,Pescador, F,Lee, JH,Sanchez, DD

Cite this

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title = "2017 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)",

abstract = "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.",

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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.

M3 - Conference Publication

BT - Real-Time Automotive Street-Scene Mapping Through Fusion of Improved Stereo Depth and Fast Feature Detection Algorithms

ER -

2017 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this