UKF‐MOT:An unscented Kalman filter‐based 3D multi‐object tracker  

作　　者：Meng Liu Jianwei Niu Yu Liu 

机构地区：[1]Collective Intelligence&Collaboration Laboratory,China North Artificial Intelligence and Innovation Research Institute,Beijing,China [2]China North Vehicle Research Institute,Beijing,China [3]State Key Laboratory of Software Development Environment,School of Computer Science and Engineering,Beihang University,Beijing,China [4]State Key Laboratory of Virtual Reality Technology and Systems,School of Computer Science and Engineering,Beihang University,Beijing,China [5]School of Computer Science and Engineering,Beihang University,Beijing,China

出　　处：《CAAI Transactions on Intelligence Technology》2024年第4期1031-1041,共11页智能技术学报（英文）

摘　　要：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 demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.

关 键 词：autonomous vehicle TRANSPORTATION 

分 类 号：TN713[电子电信—电路与系统]