Review on Environmental Perception Methods of Autonomous Vehicles

doi:10.3901/JME.2023.20.281

Abstract

Abstract: Autonomous vehicles are the future development direction of the global automotive industry and are essential for promoting the high-quality development of China's independent automotive industry. Autonomous vehicles rely on advanced technologies such as artificial intelligence and ubiquitous sensing to enhance or replace the driver's cognitive perception, decision-making planning, and control execution. Real time, accurate, and robust perception of the road environment is the cornerstone of automotive intelligence, and the huge leap in the field of autonomous vehicles perception in the past decade has been mostly driven by deep learning technology. A review of the development of autonomous vehicles environmental awareness technology in recent years is provided. Firstly, it summarizes the software and hardware architecture of autonomous vehicles environmental awareness systems, and provides an overall overview of perception, computing devices, and algorithm deployment platforms; Secondly, milestone research methods and technical solutions in recent years were summarized around four key tasks: object detection and semantic segmentation, multi objects tracking, object intention recognition and trajectory prediction, and environmental mapping; Finally, the problems and challenges faced by current autonomous vehicles environmental perception system were analyzed, and future development trends and key technologies were prospected.

Key words: autonomous vehicles, environmental perception, object detection and semantic segmentation, tracking and prediction, environmental mapping

CLC Number:

PENG Pai, GENG Keke, WANG Ziwei, LIU Zhichao, YIN Guodong. Review on Environmental Perception Methods of Autonomous Vehicles[J]. Journal of Mechanical Engineering, 2023, 59(20): 281-303.

References

[1] 李克强，戴一凡，李升波，等. 智能网联汽车(ICV)技术的发展现状及趋势[J]. 汽车安全与节能学报，2017，8(1):1-14. LI Keqiang，DAI Yifan，LI Shengbo，et al. State-of-the-art and technical trends of intelligent and connected vehicles[J]. Journal of Automotive Safety and Energy，2017，8(1):1-14.
[2] 陈虹，郭露露，宫洵，等. 智能时代的汽车控制[J]. 自动化学报，2020，46(7):1313-1332. CHEN Hong，GUO Lulu，GONG Xun，et al. Automotive control in intelligent era[J]. Acta Automatica Sinica，2020，46(7):1313-1332.
[3] 熊璐，杨兴，卓桂荣，等. 无人驾驶车辆的运动控制发展现状综述[J]. 机械工程学报，2020，56(10):127-143. XIONG Lu，YANG Xing，ZHUO Guirong，et al. Review on motion control of autonomous vehicles[J]. Journal of Mechanical Engineering，2020，56(10):127-143.
[4] 彭湃，耿可可，殷国栋，等. 基于传感器融合里程计的相机与激光雷达自动重标定方法[J]. 机械工程学报，2021，57(20):206-214. PENG Pai，GENG Keke，YIN Guodong. Automatic recalibration of camera and lidar using sensor fusion odometry[J]. Journal of Mechanical Engineering，2021，57(20):206-214.
[5] 江浩斌，沈峥楠，马世典，等. 基于信息融合的自动泊车系统车位智能识别[J]. 机械工程学报，2017，53(22):125-133. JIANG Haobin，SHEN Zhengnan，MA Shidian，et al. Intelligent identification of automatic parking system based on information fusion[J]. Journal of Mechanical Engineering，2017，53(22):125-133.
[6] 姜武华，辛鑫，陈无畏，等. 基于信息融合的自动泊车系统多工况车位识别和决策规划[J]. 机械工程学报，2021，57(6):131-141. JIANG Wuhua，XIN Xin，CHEN Wuwei，et al. Multi-condition parking space recognition based on information fusion and decision planning of automatic parking system[J]. Journal of Mechanical Engineering，2021，57(6):131-141.
[7] 王艺，蔡英凤，陈龙，等. 基于模型预测控制的智能网联汽车路径跟踪控制器设计[J]. 机械工程学报，2019，55(8):136-144. WANG Yi，CAI Yingfeng，CHEN Long，et al. Design of intelligent and connected vehicle path tracking controller based on model predictive control[J]. Journal of Mechanical Engineering，2019，55(8):136-144.
[8] 章仁燮，熊璐，余卓平. 智能汽车转向轮转角主动控制[J]. 机械工程学报，2017，53(14):106-113. ZHANG Renxie，XIONG Lu，YU Zhuoping. Active steering angle control for intelligent vehicle[J]. Journal of Mechanical Engineering，2017，53(14):106-113.
[9] LI F，SHI W，TU Y，et al. Automated methods for indoor point cloud preprocessing:Coordinate frame reorientation and building exterior removal[J]. Journal of Building Engineering，2023，76:107270.
[10] LI X，ZHANG B，SANDER P V，et al. Blind geometric distortion correction on images through deep learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:4855-4864.
[11] YAN G，LIU Z，WANG C，et al. Opencalib:A multi-sensor calibration toolbox for autonomous driving[J]. Software Impacts，2022，14:100393.
[12] PERŠIĆ J，PETROVIĆ L，MARKOVIĆ I，et al. Online multi-sensor calibration based on moving object tracking[J]. Advanced Robotics，2021，35(3-4):130-140.
[13] ERICKSON B J，KORFIATIS P，AKKUS Z，et al. Toolkits and libraries for deep learning[J]. Journal of Digital Imaging，2017，30:400-405.
[14] KIM S，WIMMER H，KIM J. Analysis of deep learning libraries:Keras，pytorch，and mxnet[C]//2022 IEEE/ACIS 20th International Conference on Software Engineering Research，Management and Applications (SERA). IEEE，2022，54-62.
[15] FELZENSZWALB P F，GIRSHICK R B，MCALLESTER D，et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2009，32(9):1627-1645.
[16] VIOLA P，JONES M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR，2001，1:511-518.
[17] PRASAD D K. Survey of the problem of object detection in real images[J]. International Journal of Image Processing (IJIP)，2012，6(6):441-466.
[18] VEDALDI A，GULSHAN V，VARMA M，et al. Multiple kernels for object detection[C]//2009 IEEE 12th International Conference on Computer Vision. IEEE，2009:606-613.
[19] VIOLA P，JONES M. Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2001，1:511-518.
[20] LIENHART R ，MAYDT J. An extended set of Haar-like features for rapid object detection[C]//International Conference on Image Processing. IEEE，2002，1:900-903.
[21] DALAL N，TRIGGS B. Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition，2005，1:886-893.
[22] FELZENSZWALB P F，GIRSHICK R B，MCALLESTER D，et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2009，32(9):1627-1645.
[23] FREUND Y，SCHAPIRE R E. Experiments with a new boosting algorithm[C]//Proceedings of the Thirteenth International Conference on International Conference on Machine Learning. 1996，96:148-156.
[24] HEARST M A，DUMAIS S T，OSUNA E，et al. Support vector machines[J]. IEEE Intelligent Systems and Their Applications，1998，13(4):18-28.
[25] VIOLA P，JONES M J. Robust real-time face detection[J]. International Journal of Computer Vision，2004，57:137-154.
[26] GIRSHICK R，DONAHUE J，DARRELL T，et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014:580-587.
[27] UIJLINGS J R R，VAN DE SANDE K E A，GEVERS T，et al. Selective search for object recognition[J]. International Journal of Computer Vision，2013，104:154-171.
[28] HE K，ZHANG X，REN S，et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2015，37(9):1904-1916.
[29] GIRSHICK R. Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015:1440-1448.
[30] REN S Q，HE K M，GIRSHICK R，et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，39(6):1137-1149.
[31] LIN T Y，DOLLÁR P，GIRSHICK R，et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:2117-2125.
[32] CAI Z，VASCONCELOS N. Cascade r-cnn:Delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:6154-6162.
[33] TERVEN J，CORDOVA-ESPARZA D. A comprehensive review of yolo:From yolov1 to yolov8 and beyond[J/OL]. ArXiv，[2023-08-07]. https://doi.org/10.48550/arXiv.2304.00501.
[34] REDMON J，DIVVALA S，GIRSHICK R，et al. You only look once:Unified，real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:779-788.
[35] LIU W，ANGUELOV D，ERHAN D，et al. Ssd:Single shot multibox detector[C]//Computer Vision–ECCV 2016:14th European Conference，Amsterdam，The Netherlands，October 11-14，2016，Proceedings，Part I 14. Springer International Publishing，2016:21-37.
[36] LAW H，TENG Y，RUSSAKOVSKY O，et al. Cornernet-lite:Efficient keypoint based object detection[J/OL]. ArXiv，[2020-09-16]. https://doi.org/10.48550/arXiv.1904.08900.
[37] DUAN K，BAI S，XIE L，et al. Centernet:Keypoint triplets for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019:6569-6578.
[38] TAN M，PANG R，LE Q V. Efficientdet:Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020:10781-10790.
[39] LI G，JI Z，QU X，et al. Cross-domain object detection for autonomous driving:A stepwise domain adaptative YOLO approach[J]. IEEE Transactions on Intelligent Vehicles，2022，7(3):603-615.
[40] XU X，ZHAO J，LI Y，et al. Banet:A balanced atrous net improved from ssd for autonomous driving in smart transportation[J]. IEEE Sensors Journal，2020，21(22):25018-25026.
[41] WANG H，XU Y，WANG Z，et al. Centernet-auto:A multi-object visual detection algorithm for autonomous driving scenes based on improved centernet[J]. IEEE Transactions on Emerging Topics in Computational Intelligence，2023，7(3):742-752.
[42] CHEN L，LIN S，LU X，et al. Deep neural network based vehicle and pedestrian detection for autonomous driving:A survey[J]. IEEE Transactions on Intelligent Transportation Systems，2021，22(6):3234-3246.
[43] LI B，OUYANG W，SHENG L，et al. Gs3d:An efficient 3d object detection framework for autonomous driving[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:1019-1028.
[44] CHABOT F，CHAOUCH M，RABARISOA J，et al. Deep manta:A coarse-to-fine many-task network for joint 2D and 3D vehicle analysis from monocular image[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:2040-2049.
[45] CHEN X，KUNDU K，ZHANG Z，et al. Monocular 3D object detection for autonomous driving[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:2147-2156.
[46] MOUSAVIAN A，ANGUELOV D，FLYNN J，et al. 3D bounding box estimation using deep learning and geometry[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:7074-7082.
[47] XIANG Y，CHOI W，LIN Y，et al. Data-driven 3D voxel patterns for object category recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015:1903-1911.
[48] XIANG Y，CHOI W，LIN Y，et al. Subcategory-aware convolutional neural networks for object proposals and detection[C]//2017 IEEE Winter Conference on Applications of Computer Vision (WACV). 2017:924-933.
[49] KUNDU A，LI Y，REHG J M. 3D-RCNN:Instance-level 3D object reconstruction via render-and-compare[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:3559-3568.
[50] CHEN X，KUNDU K，ZHU Y，et al. 3D object proposals using stereo imagery for accurate object class detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，40(5):1259-1272.
[51] GUPTA S，GIRSHICK R，ARBELÁEZ P，et al. Learning rich features from rgb-d images for object detection and segmentation[C]//Proceedings of European Conference on Computer Vision，2014:345-360.
[52] QIN Z，WANG J，LU Y. Triangulation learning network:From monocular to stereo 3D object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:7615-7623.
[53] LI P，CHEN X，SHEN S. Stereo R-CNN based 3D object detection for autonomous driving[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:7644-7652.
[54] SIMONY M，MILZY S，AMENDEY K，et al. Complex-yolo:An euler-region-proposal for real-time 3D object detection on point clouds[C]//Proceedings of the European Conference on Computer Vision (ECCV) Workshops，Munich，Germany，September 8-14，2018，Proceedings，Part I. Springer International Publishing，2019:197-209.
[55] YANG B，LUO W，URTASUN R. Pixor:Real-time 3D object detection from point clouds[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:7652-7660.
[56] SHANG J，CHEN Y，NIE J. Lasernet:A method of laser stripe center extraction under non-ideal conditions[J]. Applied Optics，2023，62(13):3387-3397.
[57] ZHOU Y，TUZEL O. Voxelnet:End-to-end learning for point cloud based 3D object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:4490-4499.
[58] YAN Y，MAO Y，LI B. Second:Sparsely embedded convolutional detection[J]. Sensors，2018，18(10):3337.
[59] HE C，ZENG H，HUANG J，et al. Structure aware single-stage 3D object detection from point cloud[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2020:11873-11882.
[60] LANG A H，VORA S，CAESAR H，et al. Pointpillars:Fast encoders for object detection from point clouds[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2019:12697-12705.
[61] QI C R，SU H，MO K，et al. Pointnet:Deep learning on point sets for 3D classification and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017:652-660.
[62] QI C R，YI L，SU H，et al. Pointnet++:Deep hierarchical feature learning on point sets in a metric space[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017:5105-5114.
[63] SHI S，WANG X，LI H. Pointrcnn:3D object proposal generation and detection from point cloud[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:770-779.
[64] CHEN X，MA H，WAN J，et al. Multi-view 3D object detection network for autonomous driving[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:1907-1915.
[65] KU J，MOZIFIAN M，LEE J，et al. Joint 3D proposal generation and object detection from view aggregation[C]//2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE，2018:1-8.
[66] SINDAGI V A，ZHOU Y，TUZEL O. Mvx-net:Multimodal voxelnet for 3D object detection[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE，2019:7276-7282.
[67] ZHOU Y，TUZEL O. Voxelnet:End-to-end learning for point cloud based 3D object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:4490-4499.
[68] QI C R，LIU W，WU C，et al. Frustum pointnets for 3D object detection from RGB-D data[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018:918-927.
[69] WANG Z，JIA K. Frustum convnet:Sliding frustums to aggregate local point-wise features for amodal 3D object detection[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE，2019:1742-1749.
[70] KIRILLOV A，HE K，GIRSHICK R，et al. Panoptic segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:9404-9413.
[71] KIRILLOV A，GIRSHICK R，HE K，et al. Panoptic feature pyramid networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:6399-6408.
[72] PORZI L，BULO S R，COLOVIC A，et al. Seamless scene segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:8277-8286.
[73] WU B，WAN A，YUE X，et al. Squeezeseg:Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3D lidar point cloud[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE，2018:1887-1893.
[74] MILIOTO A，VIZZO I，BEHLEY J，et al. Rangenet++:Fast and accurate lidar semantic segmentation[C]//2019 IEEE/RSJ International Conference on Intelligent Robots And Systems (IROS). IEEE，2019:4213-4220.
[75] LONG J，SHELHAMER E，DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015:3431-3440.
[76] RONNEBERGER O，FISCHER P，BROX T. U-net:Convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015:18th International Conference，Munich，Germany，October 5-9，2015，Proceedings，Part III 18. Springer International Publishing，2015:234-241.
[77] MILLETARI F，NAVAB N，AHMADI S A. V-net:Fully convolutional neural networks for volumetric medical image segmentation[C]//2016 Fourth International Conference on 3D Vision. 2016:565-571.
[78] BADRINARAYANAN V，KENDALL A，CIPOLLA R. Segnet:A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，39(12):2481-2495.
[79] LIN G，MILAN A，SHEN C，et al. Refinenet:Multi-path refinement networks for high-resolution semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:1925-1934.
[80] WU H，ZHANG J，HUANG K，et al. Fastfcn:Rethinking dilated convolution in the backbone for semantic segmentation[J/OL]. ArXiv，[2019-03-28]. https://doi.org/10.48550/arXiv.1903.11816.
[81] ZHAO H，SHI J，QI X，et al. Pyramid scene parsing network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:2881-2890.
[82] CHEN L C，PAPANDREOU G，KOKKINOS I，et al. Deeplab:Semantic image segmentation with deep convolutional nets，atrous convolution，and fully connected crfs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，40(4):834-848.
[83] YU F，KOLTUN V. Multi-scale context aggregation by dilated convolutions[J/OL]. ArXiv，[2016-04-30]. https://doi.org/10.48550/arXiv.1511.07122.
[84] VASWANI A，SHAZEER N，PARMAR N，et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017: 6000-6010.
[85] ZHENG S，LU J，ZHAO H，et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021:6881-6890.
[86] YASRAB R. Ecru:An encoder-decoder based convolution neural network (CNN) for road-scene understanding[J]. Journal of Imaging，2018，4(10):116-125.
[87] CHEN P R，HANG H M，CHAN S W，et al. Dsnet:An efficient cnn for road scene segmentation[J]. APSIPA Transactions on Signal and Information Processing，2020，9(27):127-137.
[88] ZHANG X，CHEN Z，WU Q M，et al. Fast semantic segmentation for scene perception[J]. IEEE Transactions on Industrial Informatics，2019，15(2):1183-1192.
[89] PENG Y，HAN W，OU Y. Semantic segmentation model for road scene based on encoder-decoder structure[C]//2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE，2019:1927-1932.
[90] CORDTS M，OMRAN M，RAMOS S，et al. The cityscapes dataset for semantic urban scene understanding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016:3213-3223.
[91] TEICHMANN M，WEBER M，ZOELLNER M，et al. Multinet:Real-time joint semantic reasoning for autonomous driving[C]//2018 IEEE Intelligent Vehicles Symposium (IV). IEEE，2018:1013-1020.
[92] GEIGER A，LENZ P，STILLER C，et al. Vision meets robotics:The kitti dataset[J]. The International Journal of Robotics Research，2013，32(11):1231-1237.
[93] HE K，GKIOXARI G，DOLLÁR P，et al. Mask R-CNN[C]//Proceedings of the IEEE International Conference On Computer Vision. 2017:2961-2969.
[94] CAI Z，VASCONCELOS N. Cascade R-CNN:High quality object detection and instance segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2019，43(5):1483-1498.
[95] HUANG Z，HUANG L，GONG Y，et al. Mask scoring R-CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:6409-6418.
[96] KIRILLOV A，WU Y，HE K，et al. Pointrend:Image segmentation as rendering[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020:9799-9808.
[97] BOLYA D，ZHOU C，XIAO F，et al. Yolact:Real-time instance segmentation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019:9157-9166.
[98] BOLYA D，ZHOU C，XIAO F，et al. Yolact++ better real-time instance segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2022，44(2):1108-1121.
[99] CHEN H，SUN K，TIAN Z，et al. Blendmask:Top-down meets bottom-up for instance segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020:8573-8581.
[100] YING H，HUANG Z，LIU S，et al. Embedmask:Embedding coupling for one-stage instance segmentation[J/OL]. ArXiv，[2019-12-05]. https://doi.org/10.48550/arXiv.1912.01954.
[101] TIAN Z，ZHANG B，CHEN H，et al. Instance and panoptic segmentation using conditional convolutions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2022，45(1):669-680.
[102] RAKAI L，SONG H，SUN S J，et al. Data association in multiple object tracking:A survey of recent techniques[J]. Expert Systems with Applications，2022，192:116300.
[103] BASHAR M，ISLAM S，HUSSAIN K K，et al. Multiple object tracking in recent times:A literature review[J/OL]. ArXiv，[2022-09-11]. https://doi.org/10.48550/arXiv.2209.04796.
[104] BEWLEY A，GE Z，OTT L，et al. Simple online and realtime tracking[C]//2016 IEEE International Conference on Image Processing (ICIP). IEEE，2016:3464-3468.
[105] WOJKE N，BEWLEY A，PAULUS D. Simple online and realtime tracking with a deep association metric[C]//2017 IEEE International Conference on Image Processing (ICIP). IEEE，2017:3645-3649.
[106] VOIGTLAENDER P，KRAUSE M，OSEP A，et al. Mots:Multi-object tracking and segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:7942-7951.