Object tracking via the probability-based segmentation using laser range images

Yung Chou Lee*, Te-Sheng Hsiao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, a probability-based segmentation approach is presented for object tracking. The proposed approach uses the Dirichlet process mixture model to describe the probabilistic distribution of observations in a single scan of a laserscanner. Then the number of segments is inferred from the observations by the Gibbs sampling method. Moreover each segment is classified into one of the three predefined classes such that most of non-vehicle-like objects on the roadsides can be filtered out. Then, the tracking algorithm, called Joint Integrated Probabilistic Data Association Filter (JIPDAF), is applied to track the classified objects and manage existing tracks. Simulations based on real traffic data demonstrate that the non-vehicle-like objects on the roadsides are suppressed. Since the number of objects in the tracking step is decreased, the computation load of the tracking step is decreased.

Original languageEnglish
Title of host publication2010 IEEE Intelligent Vehicles Symposium, IV 2010
Pages197-202
Number of pages6
DOIs
StatePublished - 17 Sep 2010
Event2010 IEEE Intelligent Vehicles Symposium, IV 2010 - La Jolla, CA, United States
Duration: 21 Jun 201024 Jun 2010

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference2010 IEEE Intelligent Vehicles Symposium, IV 2010
CountryUnited States
CityLa Jolla, CA
Period21/06/1024/06/10

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