Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (3): 97-102.doi: 10.11896/j.issn.1002-137X.2019.03.013

• ChinaMM2018 • Previous Articles     Next Articles

Improved MDP Tracking Method by Combining 2D and 3D Information

WANG Zheng-ning1,ZHOU Yang1,LV Xia1,ZENG Fan-wei1,ZHANG Xiang1,ZHANG Feng-jun2   

  1. (School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)1
    (The 30th Research Institute of CETC,Chengdu 610041,China)2
  • Received:2018-07-02 Revised:2018-09-10 Online:2019-03-15 Published:2019-03-22

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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 methodwas 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.

Key words: 2D-3D combined feature, Markov decision process, Multi-image optical flow, Multi-object tracking

CLC Number: 

  • TP181
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