Abstract：Considering that the existing multiobject tracking algorithms based on trackingbydetection framework,they often have the problems of frequent switching of object′s ID and disconnection of tracking track caused by missing detection of object or redundancy of data association algorithm.Thus,this paper proposes a multiobject vehicle and pedestrian tracking algorithm in driving scene of unmanned vehicle.Firstly,CenterNet network is selected as the object detector,and res2net embedded with 1×1 convolution and SE-Net is used to replace the original residual unit in the network,so as to improve the network′s ability to extract spatial information and channel′s information and improve the performance of the object detector.Then,siamese network is used to extract the features of the region where the target is located,and the probability of association is measured.Then,the Hungarian algorithm is used to match the detected object of adjacent frame.Finally,the auxiliary tracker designed by region proposal network is used to track the missing or disappearing objects continuously,and the reliable tracking results are incorporated into the trajectory.Compared with the existing methods,the experimental results show that the proposed method is competitive for vehicle and pedestrian tracking on the KITTI tracking benchmark dataset.
2021, Vol. 42 
