Clustering of laser measurements via the Dirichlet process mixture model for object tracking

Yung Chou Lee*, Te-Sheng Hsiao, Chih Tang Chang

*Corresponding author for this work

研究成果: Conference contribution同行評審

摘要

In this paper, the Dirichlet process mixture model is used to describe the distribution of the whole laser measurements in a given scan. Then the number of clusters is inferred from the measurements by the Gibbs sampler. We focus on the automotive application which usually has a more complex environment. Due to the variant shapes and sizes of the real traffic objects, the multi-class DP-based clustering model, which is incorporated with a mixture prior distribution, is proposed to cluster the measurements more properly. The clustering results of the proposed method are compared with those of several existing clustering methods both in an expressway case and in an urban road case. The corresponding tracking performances are also analyzed and the improvements of the proposed method are presented.

原文English
主出版物標題AIM 2012 - 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Conference Digest
頁面837-842
頁數6
DOIs
出版狀態Published - 5 十月 2012
事件2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2012 - Kaohsiung, Taiwan
持續時間: 11 七月 201214 七月 2012

出版系列

名字IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM

Conference

Conference2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2012
國家Taiwan
城市Kaohsiung
期間11/07/1214/07/12

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