Pedestrian Detection and Tracking Method for Autonomous Navigation Vehicle using Markov chain Monte Carlo Algorithm
- Pedestrian Detection and Tracking Method for Autonomous Navigation Vehicle using Markov chain Monte Carlo Algorithm
- 황중원; 김남훈; 윤정연; 김창환
- Detection and Tracking of Moving Objects; Markov Chain Monte Carlo(MCMC)
- Issue Date
- 로봇공학회논문지= The Journal of Korea Robotics Society
- VOL 7, NO 2, 113-119
- 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.
- Appears in Collections:
- KIST Publication > Article
- Files in This Item:
There are no files associated with this item.
- RIS (EndNote)
- XLS (Excel)
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.