【摘要】 在线多目标跟踪算法是自动驾驶和辅助驾驶系统的重要组成部分。目前,大部分多目标跟踪方法集中于图像域跟踪。虽然通过建立自适应在线模型或最小化能量函数可以解决大多数跟踪问题,但是如何处理复杂交通场景下目标的相互遮挡仍是研究者们面临的难题。文中基于2D和3D联合信息提出了一种改进的基于马尔科夫决策过程(MDP)的跟踪算法,通过将原始MDP跟踪算法的相似性特征由图像域拓展到空间域,使用一种新的光流特征描述子即多图像前后向跟踪误差(Multi-image FB error)来代替原算法的多区域前后向跟踪误差(Multi-aspect FB error),取得了良好的跟踪效果。最后,采用KITTI数据库对本文算法进行测试,结果显示其综合性能相较于原算法有显著提升。更多还原

【Abstract】 Online multi-object tracking(MOT) plays an important role in autonomous driving and ADAS system.Most of recent MOT methods concentrate on tracking in image domain.Although they can solve most of problems by building adaptive online models or optimizing energy functions,it’s still an obstacle for researchers to handle mutual occlusion in complex traffic scenes.In this paper,an improved tracking method was proposed by introducing 3D information to the Markov decision processes(MDP) tracker.The original MDP similarity feature was extended from image domain to spatial domain with 2D-3D combined feature,and a new optical flow descriptor,called multi-image FB error,was addressed to replace the original multi-aspect FB error.This method was tested on KITTI benchmark and the results verified that the comprehensive performance of the proposed method is refined significantly in comprehensive performance compared with the original method.更多还原