Title : ( Vehicle tracking with Kalman filter using online situation assessment )
Authors: Maryam Baradaran Khalkhali , Abedin Vahedian Mazloum , Hadi Sadoghi Yazdi ,
Full TextAbstract
Vehicle tracking is an attractive problem in the field of public transportation with several research projects conducted using Kalman filter (KF) to tackle this. While a driver may act on his own decision, there exist parameters affecting his behavior so called situation assesment such as neighboring drivers, possible obstacles, or alternative routs changing over time. In this paper, utilizing online situation assessment (SA) inside Kalman filter is studied. Motion History Graph is used as online modeling of the history of the vehicle motions and is used to augment the estimation. Experimental results on video sequences from different datasets show an average 25 percent performance improvement when using online SA inside KF.
Keywords
, Vehicle tracking, Kalman filter, Situation assessment
@article{paperid:1080262,
author = {Baradaran Khalkhali, Maryam and Vahedian Mazloum, Abedin and Sadoghi Yazdi, Hadi},
title = {Vehicle tracking with Kalman filter using online situation assessment},
journal = {Robotics and Autonomous Systems},
year = {2020},
volume = {131},
month = {September},
issn = {0921-8890},
pages = {103596--103604},
numpages = {8},
keywords = {Vehicle tracking; Kalman filter; Situation assessment},
}
%0 Journal Article
%T Vehicle tracking with Kalman filter using online situation assessment
%A Baradaran Khalkhali, Maryam
%A Vahedian Mazloum, Abedin
%A Sadoghi Yazdi, Hadi
%J Robotics and Autonomous Systems
%@ 0921-8890
%D 2020
