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Vehicle tracking with Kalman filter using online situation assessment
Robotics and Autonomous Systems ( IF 4.3 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.robot.2020.103596
Maryam Baradaran Khalkhali , Abedin Vahedian , Hadi Sadoghi Yazdi

Abstract 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 assessment such as neighboring drivers, possible obstacles, or alternative routes 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.

中文翻译:

使用在线情况评估使用卡尔曼滤波器进行车辆跟踪

摘要车辆跟踪是公共交通领域的一个有吸引力的问题,几个研究项目使用卡尔曼滤波器(KF)来解决这个问题。虽然驾驶员可以根据自己的决定采取行动,但存在影响其行为的参数,即所谓的情况评估,例如邻近驾驶员、可能的障碍物或随时间变化的替代路线。在本文中,研究了在卡尔曼滤波器中利用在线情况评估(SA)。Motion History Graph 用作车辆运动历史的在线建模,并用于增强估计。来自不同数据集的视频序列的实验结果表明,在 KF 中使用在线 SA 时,性能平均提高 25%。
更新日期:2020-09-01
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