Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (2): 49-57.doi: 10.16088/j.issn.1001-6600.2021081303

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Road Vehicle Tracking Algorithm Based on Improved YOLOv5

ZHANG Wenlong, NAN Xinyuan*   

  1. School of Electrical Engineering, Xinjiang University, Urumchi Xinjiang 830047, China
  • Received:2021-08-13 Revised:2021-09-29 Published:2022-05-31

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Abstract: To solve the problem that it's difficult for the large amount of network parameters and calculations for existing multi-object tracking algorithm to meet the real-time requirements of mobile devices, a road vehicle multi-object tracking algorithm is proposed by improving the JDE tracking algorithm. Firstly, in order to improve the tracking accuracy of the algorithm and reduce the number of ID switching, the association fusion network is used to solve the competition problem of multi-task learning in the JDE algorithm. Secondly, in order to reduce the complexity of the model and improve the real-time detection speed of the model, the improved EfficientNetV2 is used to rebuild the feature extraction network in YOLOv5. Finally, the improved YOLOv5 detection algorithm is combined with the JDE tracking algorithm to achieve multi-object tracking of road vehicles. The experimental results show that compared with the original JDE tracking algorithm, the proposed method improves MOTA by 0.3percentage point and tracking speed by about 43.2%. It can meet the speed requirements for vehicle tracking in actual autonomous driving scenarios.

Key words: vehicle tracking, EfficientNet, channel attention, associative fusion network, YOLOv5

CLC Number: 

  • TP391.41
[1] 张可, 杨灿坤, 周春平, 等. 无人机视频图像运动目标检测算法综述[J]. 液晶与显示, 2019, 34(1): 98-109. DOI: 10.3788/YJYXS20193401.0098.
[2] 张灿龙, 苏建才, 李志欣, 等. 基于AdaBoost置信图的红外与可见光目标跟踪[J]. 广西师范大学学报(自然科学版), 2018, 36(4): 42-50. DOI: 10.16088/j.issn.1001-6600.2018.04.006.
[3] 张灿龙, 李燕茹, 李志欣, 等. 基于核相关滤波与特征融合的分块跟踪算法[J]. 广西师范大学学报(自然科学版), 2020, 38(5): 12-23. DOI: 10.16088/j.issn.1001-6600.2020.05.002.
[4] 黄一凡, 杨建业, 戚国庆. 基于二自由度转台的双目视觉跟踪技术研究[J]. 电子设计工程, 2018, 26(14):181-185. DOI: 10.14022/j.cnki.dzsjgc.2018.14.039.
[5] 冯霞, 杜佳浩, 段仪浓, 等. 基于深度学习的行人重识别研究综述[J]. 计算机应用研究, 2020, 37(11): 3220-3226, 3240. DOI: 10.19734/j.issn.1001-3695.2019.09.0514.
[6] 尹梓睿, 张索非, 张磊, 等. 适于行人重识别的二分支EfficientNet网络设计[J]. 信号处理, 2020, 36(9): 1481-1488. DOI: 10.16798/j.issn.1003-0530.2020.09.013.
[7] 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). Piscataway, NJ: IEEE Press, 2017: 3645-3649. DOI: 10.1109/ICIP.2017.8296962.
[8] ZHOU Z W, XING J L, ZHANG M D, et al. Online multi-target tracking with tensor-based high-order graph matching[C]// 2018 24th International Conference on Pattern Recognition(ICPR). Piscataway, NJ: IEEE Press, 2018: 1809-1814. DOI: 10.1109/ICPR.2018.8545450.
[9] WANG Z D, ZHENG L, LIU Y X, et al. Towards real-time multi-object tracking[EB/OL]. (2020-07-14)[2021-08-13]. https://arxiv.org/abs/1909.12605.
[10] REDMON J, FARHADI A. YOLOv3: An incremental improvement[EB/OL]. (2018-04-08)[2021-08-13]. https://arxiv.org/abs/1804.02767v1.
[11] ZHANG Y F, WANG C Y, WANG X G, et al. FairMOT: on the fairness of detection and re-identification in multiple object tracking[J]. International Journal of Computer Vision, 2021, 129(11): 3069-3087. DOI: 10.1007/s11263-021-01513-4.
[12] YU F, WANG D Q, SHELHAMER E, et al. Deep layer aggregation[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 2018: 2403-2412. DOI: 10.1109/CVPR.2018.00255.
[13] ZHOU X Y, WANG D Q, KRÄHENBÜHL P. Objects as points[EB/OL]. (2019-04-25)[2021-08-13]. https://arxiv.org/abs/1904.07850.
[14] 晏康, 曾凤彩, 何宁, 等. 引入注意力机制的JDE多目标跟踪方法[J/OL]. 计算机工程与应用, 2021[2021-08-13]. http://kns.cnki.net/kcms/detail/11.2127.TP.20210524.0920.004.html. DOI: 10.3778/j.issn.1002-8331.2104-0049.
[15] 薛俊韬, 马若寒, 胡超芳. 基于MobileNet的多目标跟踪深度学习算法[J]. 控制与决策, 2021, 36(8): 1991-1996. DOI: 10.13195/j.kzyjc.2019.1424.
[16] SANDLER M, HOWARD A, ZHU M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 2018: 4510-4520. DOI: 10.1109/CVPR.2018.00474.
[17] 马永杰, 马芸婷, 程时升, 等. 基于改进YOLO v3模型与Deep-SORT算法的道路车辆检测方法[J]. 交通运输工程学报, 2021, 21(2): 222-231. DOI: 10.19818/j.cnki.1671-1637.2021.02.019.
[18] HU J, SHEN L, AIBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023. DOI: 10.1109/TPAMI.2019.2913372.
[19] WANG Q L, WU B G, ZHU P F, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2020: 11531-11539. DOI: 10.1109/CVPR42600.2020.01155.
[20] QIN Z Q, ZHANG P Y, WU F, et al. FCANet: frequency channel attention networks[EB/OL]. (2021-07-23)[2021-08-13]. https://arxiv.org/abs/2012.11879.
[21] WOO S H, PARK J C, LEE J Y, et al. CBAM: convolutional block attention module[C]// Computer Vision-ECCV 2018: LNCS volume 11211. Cham: Springer Nature Switzerland AG, 2018: 3-19. DOI: 10.1007/978-3-030-01234-2_1.
[22] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2017: 936-944. DOI: 10.1109/CVPR.2017.106.
[23] TAN M X, LE Q V. EfficientNetV2: smaller models and faster training[EB/OL]. (2021-06-23)[2021-08-13]. https://arxiv.org/abs/2104.00298.
[24] FU J, LIU J, TIAN H J, et al. Dual attention network for scene segmentation[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE Computer Society, 2019: 3141-3149. DOI: 10.1109/CVPR.2019.00326.
[25] XU D,OUYANG W L, WANG X G, et al. PAD-Net: multi-tasks guided prediction-and-distillation network for simultaneous depth estimation and scene parsing[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 2018: 675-684. DOI: 10.1109/CVPR.2018.00077.
[26] LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos, CA: IEEE Computer Society, 2018: 8759-8768. DOI: 10.1109/CVPR.2018.00913.
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