一种基于2D和3D联合信息的改进MDP跟踪算法
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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。
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.
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.
引文
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[2] HE K,ZHANG X,REN S,et al.Deep Residual Learning for Ima- ge Recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2016:770-778.
[3] ROTH S,ANDRIYENKO A,SCHINDLER K.Discrete-conti- nuous optimization for multi-target tracking[C]//Computer Vision and Pattern Recognition.IEEE,2012:1926-1933.
[4] MILAN A,ROTH S,SCHINDLER K.Continuous optimization for multi-target tracking [J].IEEE transactions on pattern ana-lysis and machine intelligence,2014,36(1):58-72.
[5] ZHANG L,LI Y,NEVATIA R.Global data association for multi-object tracking using network flows[C]//IEEE Con-ference on Computer Vision and Pattern Recognition.IEEE,2008:1-8.
[6] SONG X,CUI J,ZHA H,et al.Vision-Based Multiple Interacting Targets Tracking via On-Line Supervised Learning[C]//European Conference on Computer Vision.Springer-Verlag,2008:642-655.
[7] CHOI W,PANTOFARU C,SAVARESE S.A general framework for tracking multiple people from a moving camera[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(7):1577-1591.
[8] JU H Y,LEE C R,YANG M H,et al.Online Multi-object Tracking via Structural Constraint Event Aggregation[C]//Computer Vision and Pattern Recognition.IEEE,2016:1392-1400.
[9] CHOI W.Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor[C]//IEEE International Conference on Computer Vision.IEEE,2015:3029-3037.
[10] BERCLAZ J,FLEURET F,TURETKEN E,et al.Multiple object tracking using k-shortest paths optimization[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(9):1806-1819.
[11] KALAL Z,MIKOLAJCZYK K,MATAS J.Forward-backward error:Automatic detection of tracking failures[C]//2010 20th International Conference on Pattern recognition (ICPR).IEEE,2010:2756-2759.
[12] XIANG Y,ALAHI A,SAVARESE S.Learning to track:Online multi-object tracking by decision making[C]//2015 IEEE International Conference on Computer Vision (ICCV).IEEE,2015:4705-4713.
[13] LI Y,HUANG C,NEVATIA R.Learning to associate:Hybridboosted multi-target tracker for crowded scene[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:2953-2960.
[14] HENRIQUES J F,RUI C,MARTINS P,et al.Exploiting the Circulant Structure of Tracking-by-Detection with Kernels[C]//European Conference on Computer Vision.Springer-Verlag,2012:702-715.
[15] HENRIQUES J F,RUI C,MARTINS P,et al.High-Speed Tracking with Kernelized Correlation Filters[J].IEEE Transa-ctions on Pattern Analysis & Machine Intelligence,2015,37(3):583-596.
[16] BELLMAN R.A Markovian decision process[J].Journal of Mathematics and Mechanics,1957,6(5):679-684.
[17] KALAL Z,MIKOLAJCZYK K,MATAS J.Tracking-learning- detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(7):1409-1422.
[18] BERNARDIN K,STIEFELHAGEN R.Evaluating multiple object tracking performance:the CLEAR MOT metrics[OL].http://doi.org/10.1155/2008/246309.
[19] LI Y,HUANG C,NEVATIA R.Learning to associate:Hybridboosted multi-target tracker for crowded scene[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:2953-2960.
[20] GEIGER A,LENZ P,URTASUN R.Are we ready for autonomous driving? thekitti vision benchmark suite[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2012:3354-3361.
[21] O?EP A,MEHNER W,MATHIAS M,et al.Combined image-and world-space tracking in traffic scenes[C]//IEEE International Conference on Robotics and Automation.2017:1988-1995.
[22] CHOI W.Near-online multi-target tracking with aggregated local flow descriptor[C]//Proceedings of the IEEE International Conference on Computer Vision.IEEE,2015:3029-3037.
[23] ANDRIYENKO A,SCHINDLER K,ROTH S.Discrete-conti- nuous optimization for multi-target tracking[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2012:1926-1933.
[24] GEIGER A,LAUER M,WOJEK C,et al.3D Traffic Scene Understanding From Movable Platforms[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2014,36(5):1012-1025.
[25] ZHANG H,GEIGER A,URTASUN R.Understanding High- Level Semantics by Modeling Traffic Patterns[C]//IEEE International Conference on Computer Vision.IEEE,2013:3056-3063.
[26] XIANG Y,ALAHI A,SAVARESE S.Learning to track:Online multi-object tracking by decision making[C]//Proceedings of the IEEE International Conference on Computer Vision.IEEE,2015:4705-4713.