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

Yung Chou Lee*, Te-Sheng Hsiao

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2010 IEEE Intelligent Vehicles Symposium, IV 2010
頁面197-202
頁數6
DOIs
出版狀態Published - 17 9月 2010
事件2010 IEEE Intelligent Vehicles Symposium, IV 2010 - La Jolla, CA, United States
持續時間: 21 6月 201024 6月 2010

出版系列

名字IEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference2010 IEEE Intelligent Vehicles Symposium, IV 2010
國家/地區United States
城市La Jolla, CA
期間21/06/1024/06/10

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