Detection of Cars in Mobile Lidar Point Clouds

Guorui Li, Xinwei Fang, Kourosh Khoshelham, S.J. Oude Elberink

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

1 Citation (Scopus)

Abstract

This paper describes a method for automated detection of temporary cars in Mobile LiDAR point clouds. It consists of a segment-based classification of static cars and a comparison of data from two sensors to identify moving cars. Two segmentation methods are used to extract the ground and group the above-ground points into objects. From each segmented object a number of features are extracted, and a classification strengthened by feature selection is performed to classify temporary cars. We evaluate the performance of two different classifiers trained with a training set including 117 temporary cars, and show classification accuracies of up to 92%. We also investigate a method for identifying moving cars based on the distance between corresponding segments in the point clouds captured by the two scanning sensors, and report an overall accuracy of 61%.
Original languageEnglish
Title of host publication2018 3rd International Conference on Intelligent Transportation Engineering
PublisherIEEE
Pages259-263
Number of pages5
ISBN (Electronic)978-1-5386-7831-2
DOIs
Publication statusPublished - Sep 2018
Event2018 3rd IEEE International Conference on Intelligent Transportation Engineering - Singapore, Singapore
Duration: 3 Sep 20185 Sep 2018
Conference number: 3

Conference

Conference2018 3rd IEEE International Conference on Intelligent Transportation Engineering
Abbreviated titleICITE 2018
CountrySingapore
CitySingapore
Period3/09/185/09/18

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