A Model-based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds

Börcs, Attila and Nagy, Balázs and Baticz, Milán and Benedek, Csaba (2014) A Model-based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds. In: Workshop on Scene Understanding for Autonomous Systems at ACCV, Singapore.

[img]
Preview
Text
borcs_accvWS2014.pdf

Download (2MB) | Preview

Abstract

Detection of vehicles in crowded 3-D urban scenes is a challenging problem in many computer vision related research fields, such as robot perception, autonomous driving, self-localization, and mapping. In this paper we present a model-based approach to solve the recognition problem from 3-D range data. In particular, we aim to detect and recognize vehicles from continuously streamed LIDAR point cloud sequences of a rotating multi-beam laser scanner. The end-to-end pipeline of our framework working on the raw streams of 3-D urban laser data consists of three steps 1) producing distinct groups of points which represent different urban objects 2) extracting reliable 3-D shape descriptors specifically designed for vehicles, considering the need for fast processing speed 3) executing binary classification on the extracted descriptors in order to perform vehicle detection. The extraction of our efficient shape descriptors provides a significant speedup with and increased detection accuracy compared to a PCA based 3-D bounding box fitting method used as baseline.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Distributed Events Analysis Research Laboratory
Depositing User: Csaba Benedek
Date Deposited: 08 Oct 2014 09:50
Last Modified: 08 Oct 2014 09:50
URI: http://eprints.sztaki.hu/id/eprint/8029

Update Item Update Item