Pedestrian Detection and Tracking Method for Autonomous Navigation Vehicle using Markov chain Monte Carlo Algorithm

Title
Pedestrian Detection and Tracking Method for Autonomous Navigation Vehicle using Markov chain Monte Carlo Algorithm
Authors
황중원김남훈윤정연김창환
Keywords
Detection and Tracking of Moving Objects; Markov Chain Monte Carlo(MCMC)
Issue Date
2012-06
Publisher
로봇공학회논문지= The Journal of Korea Robotics Society
Citation
VOL 7, NO 2, 113-119
Abstract
In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.
URI
http://pubs.kist.re.kr/handle/201004/44305
ISSN
19756291
Appears in Collections:
KIST Publication > Article
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