基于自查询的车载多目标跟踪算法研究

doi:10.19562/j.chinasae.qcgc.2021.11.003

Abstract

Abstract:

In order to balance the tracking performance （i.e. the indicators of MOTA， MOTP and IDSW etc.） and tracking speed， especially， to solve the complexity of the post processing for video multi-target tracking， a multi-target tracking method based on autoregressive query mechanism is proposed， with training and verification conducted. The results of verification show that the inference of each frame of picture takes about 44 ms， and the accuracy of multi-target tracking reaches 58.9%. The model is integrated into the ROS platform of intelligent vehicle for testing and the results of test indicate that the algorithm proposed can achieve multi-target real-time tracking in complex traffic scenes， and the algorithm has good practical application value.

Key words: onboard multi-object tracking, deep learning, self-query tracking

Long Chen,Chengzheng Zhu,Yingfeng Cai,Hai Wang,Yicheng Li. Research on Vehicle Multi-Target Environment Aware Tracking Algorithm Based on Self-Query[J].Automotive Engineering, 2021, 43(11): 1587-1593.

Figures/Tables 6

References 18

1	XU Y， OSEP A， BAN Y， et al. How to train your deep multi-object tracker［C］.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition， 2020： 6787-6796.
2	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.
3	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.
4	WANG Z， ZHENG L， LIU Y， et al. Towards real-time multi-object tracking［J］. arXiv preprint arXiv：1909.12605， 2019， 2（3）： 4.
5	ZHOU X， KOLTUN V， KRÄHENBÜHL P. Tracking objects as points［C］.European Conference on Computer Vision. Springer， Cham， 2020： 474-490.
6	VASWANI A， SHAZEER N， PARMAR N， et al. Attention is all you need［J］. arXiv preprint arXiv：， 2017.
7	QUIGLEY M， CONLEY K， GERKEY B， et al. ROS： an open-source robot operating system［C］.ICRA Workshop on Open Source Software， 2009， 3（3.2）： 5.
8	HE K， ZHANG X， REN S， et al. Deep residual learning for image recognition［C］.Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition， 2016： 770-778.
9	ZHOU X， WANG D， KRÄHENBÜHL P. Objects as points［J］. arXiv preprint arXiv：， 2019.
10	LIN T Y， GOYAL P， GIRSHICK R， et al. Focal loss for dense object detection［C］.Proceedings of the IEEE International Conference on Computer Vision， 2017： 2980-2988.
11	BOCHINSKI E， EISELEIN V， SIKORA T. High-speed tracking-by-detection without using image information［C］.2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance （AVSS）. IEEE， 2017： 1-6.
12	REZATOFIGHI H， TSOI N， GWAK J Y， et al. Generalized intersection over union： a metric and a loss for bounding box regression［C］.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition， 2019： 658-666.
13	ZHENG Z， WANG P， LIU W， et al. Distance-IoU loss： faster and better learning for bounding box regression［C］.Proceedings of the AAAI Conference on Artificial Intelligence， 2020， 34（7）： 12993-13000.
14	LEAL-TAIXÉ L， MILAN A， REID I， et al. Motchallenge 2015： towards a benchmark for multi-target tracking［J］. arXiv preprint arXiv：， 2015.
15	DENDORFER P， REZATOFIGHI H， MILAN A， et al. Mot20： a benchmark for multi object tracking in crowded scenes［J］. arXiv preprint arXiv：， 2020.
16	MALLA S， DARIUSH B， CHOI C. Titan： future forecast using action priors［C］.Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition， 2020： 11186-11196.
17	YU F， CHEN H， WANG X， et al. Bdd100k： a diverse driving dataset for heterogeneous multitask learning［C］.Proceedings of the IEEE/CVF Conference on Computer Vision And Pattern Recognition， 2020： 2636-2645.
18	ZHANG Y， WANG C， WANG X， et al. FairMOT： on the fairness of detection and re-identification in multiple object tracking［J］. arXiv preprint arXiv：， 2020.

数据集	多目标跟踪准度		识别F值		身份切换次数		跟踪完整率/%		秒运行帧数/fps
	文献［18］	本文	文献［18］	本文	文献［18］	本文	文献［18］	本文	文献［18］	本文
MOT15	60.6	59.4	64.7	75.3	591	154	47.6	49.5	30.5	44.6
MOT17	73.7	73.2	72.3	82.4	3303	1907	44.7	47.3	25.9	31.4
MOT20	61.8	58.9	67.3	69.5	5243	2239	68.8	85.1	13.2	22.7

[1]	Ze Gao, Zunkang Chu, Jiasheng Shi, Fu Lin, Weixiong Rao, Haiyan Yu. Research on Fast Prediction Method of Stress Field of Automotive Parts Based on Graph Network [J]. Automotive Engineering, 2024, 46(1): 170-178.
[2]	Weiguo Liu,Zhiyu Xiang,Rui Liu,Guodong Li,Zixu Wang. Research on End-to-End Vehicle Motion Planning Method Based on Deep Learning [J]. Automotive Engineering, 2023, 45(8): 1343-1352.
[3]	Jiahao Zhao,Zhiquan Qi,Zhifeng Qi,Hao Wang,Lei He. Calculation of Heading Angle of Parallel Large Vehicle Based on Tire Feature Points [J]. Automotive Engineering, 2023, 45(6): 1031-1039.
[4]	Yanyan Chen,Hai Wang,Yingfeng Cai,Long Chen,Yicheng Li. Efficient Automatic Driving Instance Segmentation Method Based on Detection [J]. Automotive Engineering, 2023, 45(4): 541-550.
[5]	Fengchong Lan,Jikai Chen,Jiqing Chen,Xinping Jiang,Zihan Li,Wei Pan. Research on Lithium Battery Remaining Useful Life Prediction Method Driven by Real Vehicle Data [J]. Automotive Engineering, 2023, 45(2): 175-182.
[6]	Linhui Li,Xinliang Zhang,Yifan Fu,Jing Lian,Jiaxu Ma. Research on Visible Light and Infrared Post-Fusion Detection Based on TC-YOLOv7 Algorithm [J]. Automotive Engineering, 2023, 45(12): 2280-2290.
[7]	Xiaojun Zhang,Jingzhe Xi,Yanlei Shi,Anlu Yuan. Lightweight YOLOv7-R Algorithm for Road-Side View Target Detection [J]. Automotive Engineering, 2023, 45(10): 1833-1844.
[8]	Jie Hu,Yuanjie Li,Hao Geng,Huangzheng Geng,Xiong Guo,Hongwei Yi. Construction of Vehicle Fault Knowledge Graph Based on Deep Learning [J]. Automotive Engineering, 2023, 45(1): 52-60.
[9]	Jie Hu,Boyuan Xu,Zongquan Xiong,Minjie Chang,Di Guo,Lihao Xie. Cross-Domain Object Detection Algorithm Based on Multi-scale Mask Classification Domain Adaptive Network [J]. Automotive Engineering, 2022, 44(9): 1327-1338.
[10]	Guihong Bi,Xu Xie,Zilong Cai,Zhao Luo,Chenpeng Chen,Xin Zhao. Capacity Estimation of Lithium-ion Battery Based on Deep Learning Under Dynamic Conditions [J]. Automotive Engineering, 2022, 44(6): 868-878.
[11]	Dafang Wang,Jingdong Du,Jiang Cao,Mei Zhang,Gang Zhao. Research on Style Transfer Network for Autonomous Driving Data Generation [J]. Automotive Engineering, 2022, 44(5): 684-690.
[12]	Jinghua Guo,Zhifei He,Yugong Luo,Keqiang Li. Vehicle Cut-in Trajectory Prediction Based on Deep Learning in a Human-machine Mixed Driving Environment [J]. Automotive Engineering, 2022, 44(2): 153-160.
[13]	Yuande Jiang,Bing Zhu,Xiangmo Zhao,Jian Zhao,Bingbing Zheng. Modeling of Traffic Vehicle Interaction for Autonomous Vehicle Testing [J]. Automotive Engineering, 2022, 44(12): 1825-1833.
[14]	Zhenhai Gao,Wenhao Wen,Minghong Tang,Jian Zhang,Guoying Chen. Estimation of Vehicle Motion State Based on Hybrid Neural Network [J]. Automotive Engineering, 2022, 44(10): 1527-1536.
[15]	Shaowei Zhang,Dawei Zhu,Guangzhao Zhai. Prediction on Seat’s Anti-whiplash-injury Performance Based on Deep Learning [J]. Automotive Engineering, 2022, 44(10): 1600-1608.

Research on Vehicle Multi-Target Environment Aware Tracking Algorithm Based on Self-Query

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 18

Related Articles 15

Metrics

Comments

Recommended 10