{"title":"UKF-MOT: An unscented Kalman filter-based 3D multi-object tracker","authors":"Meng Liu, Jianwei Niu, Yu Liu","doi":"10.1049/cit2.12315","DOIUrl":null,"url":null,"abstract":"<p>Multi-object tracking in autonomous driving is a non-linear problem. To better address the tracking problem, this paper leveraged an unscented Kalman filter to predict the object's state. In the association stage, the Mahalanobis distance was employed as an affinity metric, and a Non-minimum Suppression method was designed for matching. With the detections fed into the tracker and continuous ‘predicting-matching’ steps, the states of each object at different time steps were described as their own continuous trajectories. We conducted extensive experiments to evaluate tracking accuracy on three challenging datasets (KITTI, nuScenes and Waymo). The experimental results demonstrated that our method effectively achieved multi-object tracking with satisfactory accuracy and real-time efficiency.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 4","pages":"1031-1041"},"PeriodicalIF":8.4000,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12315","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CAAI Transactions on Intelligence Technology","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cit2.12315","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Multi-object tracking in autonomous driving is a non-linear problem. To better address the tracking problem, this paper leveraged an unscented Kalman filter to predict the object's state. In the association stage, the Mahalanobis distance was employed as an affinity metric, and a Non-minimum Suppression method was designed for matching. With the detections fed into the tracker and continuous ‘predicting-matching’ steps, the states of each object at different time steps were described as their own continuous trajectories. We conducted extensive experiments to evaluate tracking accuracy on three challenging datasets (KITTI, nuScenes and Waymo). The experimental results demonstrated that our method effectively achieved multi-object tracking with satisfactory accuracy and real-time efficiency.
期刊介绍:
CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.