驾驶场景下结合运动速度以及外观特征的多类多目标跟踪方法

doi:10.19562/j.chinasae.qcgc.2024.06.002

Abstract

Abstract:

Multi-target tracking algorithms based on camera sensors are crucial to autonomous driving. In driving scenarios， traditional association schemes based on Intersection over Union（IoU） of front and back frames are subject to a great deal of ID switches， which is more pronounced in the case of opposing traffic and self-turning vehicles. In this paper， the target's motion speed in the 2D plane is taken as a variable to extend the matching space to design IoU based on the target's speed change： the Velocity IoU， so as to optimize the front and back frame target association method. Meanwhile， a self-supervised appearance model is used to extract the appearance features of different targets. Based on the above motion model as well as the appearance model， a complementary association strategy is proposed， which ultimately achieves multi-category multi-target tracking in driving scenarios. The method is validated on BDD100K， with corresponding mMOTA of 45.2， mIDF1 of 55.2， and IDs of 8 793， which outperforms most tracking algorithms.

Key words: autonomous driving, multi-target tracking, multi-category

Hai Wang,Yuxuan Ding,Tong Luo,Meng Qiu,Yingfeng Cai,Long Chen. A Multi-class Multi-target Tracking Algorithm Combining Motion Speed and Appearance Features in Driving Scenarios[J].Automotive Engineering, 2024, 46(6): 956-964.

Figures/Tables 11

References 30

1	李磊磊. 自动驾驶汽车产业发展研究及展望［J］. 汽车文摘， 2023（9）： 1-10.
	LI L L. Development research and prospect on automated driving vehicle industry［J］. Automative Digest， 2023（9）： 1-10.
2	PARK Y， DANG L M， LEE S， et al. Multiple object tracking in deep learning approaches： a survey ［J］. Electronics， 2021， 10（19）： 2406.
3	ZHOU X， WANG D， KRÄHENBÜHL P. Objects as points ［J］. arXiv preprint arXiv：， 2019.
4	BOCHKOVSKIY A， WANG C Y， LIAO H Y M. Yolov4： optimal speed and accuracy of object detection ［J］. arXiv preprint arXiv：， 2020.
5	FARHADI A， REDMON J. Yolov3： an incremental improvement［C］. Proceedings of the Computer Vision and Pattern Recognition， F， 2018. Springer Berlin/Heidelberg， Germany.
6	SUN P， ZHANG R， JIANG Y， et al. Sparse R-CNN： end-to-end object detection with learnable proposals［C］. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition， F， 2021.
7	BEWLEY A， GE Z， OTT L， et al. Simple online and realtime tracking［C］. Proceedings of the 2016 IEEE International Conference on Image Processing （ICIP）， F， 2016. IEEE.
8	ZHANG Y， SUN P， JIANG Y， et al. Bytetrack： multi-object tracking by associating every detection box［C］. Proceedings of the European Conference on Computer Vision， F， 2022. Springer.
9	ZHOU X， KOLTUN V， KRäHENBüHL P. Tracking objects as points［C］. Proceedings of the European Conference on Computer Vision， F， 2020. Springer.
10	BERGMANN P， MEINHARDT T， LEAL-TAIXE L. Tracking without bells and whistles［C］. Proceedings of the Proceedings of the IEEE/CVF International Conference on Computer Vision， F， 2019.
11	MEINHARDT T， KIRILLOV A， LEAL-TAIXE L， et al. Trackformer： multi-object tracking with transformers［C］. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition， F， 2022.
12	SUN P， CAO J， JIANG Y， et al. Transtrack： multiple object tracking with transformer ［J］. arXiv preprint arXiv：， 2020.
13	ZENG F， DONG B， ZHANG Y， et al. MOTR： end-to-end multiple-object tracking with transformer［C］. Proceedings of the Computer Vision-ECCV 2022： 17th European Conference， Tel Aviv， Israel， October 23-27， 2022， Proceedings， Part XXVII， F， 2022. Springer.
14	WOJKE N， BEWLEY A， PAULUS D. Simple online and realtime tracking with a deep association metric［C］. Proceedings of the 2017 IEEE International Conference on Image Processing （ICIP）， F， 2017. IEEE.
15	PANG J， QIU L， LI X， et al. Quasi-dense similarity learning for multiple object tracking［C］. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition， F， 2021.
16	ZHANG Y， WANG C， WANG X， et al. Fairmot： on the fairness of detection and re-identification in multiple object tracking ［J］. International Journal of Computer Vision， 2021， 129： 3069-3087.
17	LIANG C， ZHANG Z， ZHOU X， et al. Rethinking the competition between detection and reid in multiobject tracking ［J］. IEEE Transactions on Image Processing， 2022， 31： 3182-3196.
18	MILAN A， LEAL-TAIXé L， REID I， et al. MOT16： a benchmark for multi-object tracking ［J］. arXiv preprint arXiv：， 2016.
19	AHARON N， ORFAIG R， BOBROVSKY B Z. BoT-SORT： robust associations multi-pedestrian tracking ［J］. arXiv preprint arXiv：， 2022.
20	CAO J， WENG X， KHIRODKAR R， et al. Observation-Centric SORT： rethinking SORT for robust multi-object tracking ［J］. arXiv preprint arXiv：， 2022.
21	XU J， WANG X. Rethinking self-supervised correspondence learning： a video frame-level similarity perspective［C］. Proceedings of the IEEE/CVF International Conference on Computer Vision， F， 2021.
22	WANG Z， ZHENG L， LIU Y， et al. Towards real-time multi-object tracking［C］. Proceedings of the European Conference on Computer Vision， F， 2020. Springer.
23	BROWN R G， HWANG P Y. Introduction to random signals and applied Kalman filtering： with MATLAB exercises and solutions ［J］. Introduction to Random Signals and Applied Kalman Filtering： with MATLAB Exercises and Solutions， 1997.
24	LEAL-TAIXé L， PONS-MOLL G， ROSENHAHN B. Everybody needs somebody： modeling social and grouping behavior on a linear programming multiple people tracker［C］. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops （ICCV workshops）， F， 2011. IEEE.
25	YANG F， ODASHIMA S， MASUI S， et al. Hard to track objects with irregular motions and similar appearances？ make it easier by buffering the matching space［C］. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision， F， 2023.
26	WANG Z， ZHAO H， LI Y L， et al. Do different tracking tasks require different appearance models？［J］. Advances in Neural Information Processing Systems， 2021， 34： 726-738.
27	YU F， XIAN W， CHEN Y， et al. BDD100K： a diverse driving video database with scalable annotation tooling ［J］. arXiv preprint arXiv：， 2018.
28	BERNARDIN K， STIEFELHAGEN R. Evaluating multiple object tracking performance： the clear mot metrics ［J］. EURASIP Journal on Image Video Processing， 2008， 2008： 1-10.
29	RISTANI E， SOLERA F， ZOU R， et al. Performance measures and a data set for multi-target， multi-camera tracking［C］. Proceedings of the European Conference on Computer Vision， F， 2016. Springer.
30	CHEN L， AI H， ZHUANG Z， et al. Real-time multiple people tracking with deeply learned candidate selection and person re-identification［C］. Proceedings of the 2018 IEEE International Conference on Multimedia and Expo （ICME）， F， 2018. IEEE.

数据集	模型	mMOTA↑	mIDF1↑	IDs↓
BDD100K	Baseline	39.3	48.2	28 120
BDD100K	Ours	45.2（+5.9）	55.2（+7.0）	8 793（-19 327）

模型	mMOTA↑	mIDF1↑	IDs↓
SORT	30.9	41.3	10 067
MOTDT^［30］	26.7	39.8	14 520
QDTrack	36.3	51.5
GHOST	44.9	55.6
CO-MOT*	34.2	48.2
ByteTrack	45.5	54.8	9 140
MOTR*	35.5	48.2
MOTRv2*	41.7	53.5
DeepSORT	24.5	38.2	10 720
Unicorn	41.2	54.0
VTrack（ours）	45.2	55.2	8 793

模型	线性运动模型	互补关联策略	Velocity IoU	mMOTA↑	mIDF1↑	IDs↓
Baseline				39.3	48.2	28 120
Baseline+col 1	√			39.2	48.5	27 843
Baseline+col 1-2	√	√		44.7	54.4	9 695
Baseline+col 1-3	√	√	√	45.2	55.2	8 793

放大比例	BDD100K-val
放大比例	mMOTA↑	mIDF1↑
c=0	39.3	48.2
c=0.1	39.5	49.2
c=0.2	40.1	49.8
c=0.3	38.9	48.1
c=0.4	38.2	46.7

[1]	Jing Huang,Xiangzhen Liu,Xiaoyang Deng,Ran Chen. Research on Intelligent Vehicle Trajectory Planning Based on Multimodal Trajectory Prediction [J]. Automotive Engineering, 2024, 46(6): 965-974.
[2]	Mengfan Li,Zhongxiang Feng,Weihua Zhang,Jingyu Li. Study on Driver's Visual Transfer Characteristics During the Takeover Process of Human-Computer Co-driving Mode [J]. Automotive Engineering, 2024, 46(5): 795-804.
[3]	Fuxing Yao,Chao Sun,Yungang Lan,Bing Lu,Bo Wang,Haiyang Yu. A Lane Change Decision Method for Intelligent Connected Vehicles Based on Mixture of Expert Model [J]. Automotive Engineering, 2024, 46(5): 882-892.
[4]	Ting Chikit,Yafei Wang,Yichen Zhang,Mingyu Wu,Yile Wang. Energy-Saving Planning Method for Autonomous Driving Mining Trucks Based on Composite Dynamic Sampling [J]. Automotive Engineering, 2024, 46(4): 588-595.
[5]	Yiwei Zhou,Mo Xia,Bing Zhu. Multimodal Vehicle Trajectory Prediction Methods Considering Multiple Traffic Participants in Urban Road Scenarios [J]. Automotive Engineering, 2024, 46(3): 396-406.
[6]	Xiaocong Zhao,Shiyu Fang,Zirui Li,Jian Sun. Extraction and Application of Key Utility Term for Social Driving Interaction [J]. Automotive Engineering, 2024, 46(2): 230-240.
[7]	Yanli Ma, Qin Qin, Fangqi Dong, Yining Lou. Takeover Risk Assessment Model Based on Risk Field Theory Under Different Cognitive Secondary Tasks [J]. Automotive Engineering, 2024, 46(1): 9-17.
[8]	Weiguo Liu,Zhiyu Xiang,Weiping Liu,Daoxin Qi,Zixu Wang. Research on Vehicle Control Algorithm Based on Distributed Reinforcement Learning [J]. Automotive Engineering, 2023, 45(9): 1637-1645.
[9]	Ming Wang,Xiaolin Tang,Kai Yang,Guofa Li,Xiaosong Hu. A Motion Planning Method for Autonomous Vehicles Considering Prediction Risk [J]. Automotive Engineering, 2023, 45(8): 1362-1372.
[10]	Dongyu Zhao, Shuen Zhao. Autonomous Driving 3D Object Detection Based on Cascade YOLOv7 [J]. Automotive Engineering, 2023, 45(7): 1112-1122.
[11]	Yong Han,Xujie Lin,Hongwu Huang,Hongyu Cai,Jinrong Luo,Yangting Li. An Approach for Predicting Pedestrian Trajectories in Typical Car Crash Scenarios [J]. Automotive Engineering, 2023, 45(6): 1022-1030.
[12]	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.
[13]	Lisheng Jin,Guangde Han,Xianyi Xie,Baicang Guo,Guofeng Liu,Wentao Zhu. Review of Autonomous Driving Decision-Making Research Based on Reinforcement Learning [J]. Automotive Engineering, 2023, 45(4): 527-540.
[14]	Qingyang Huang,Xiaoping Jin,Yikang Zhang. Analysis of Drivers' Driving Posture Change Rule Under the Condition of Automatic Driving Level Improvement [J]. Automotive Engineering, 2023, 45(3): 382-392.
[15]	Zhengfa Liu,Ya Wu,Peigen Liu,Rongqi Gu,Guang Chen. Cross-Domain Object Detection for Intelligent Driving Based on Joint Distribution Matching of Features and Labels [J]. Automotive Engineering, 2023, 45(11): 2082-2091.

A Multi-class Multi-target Tracking Algorithm Combining Motion Speed and Appearance Features in Driving Scenarios

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 30

Related Articles 15

Metrics

Comments

Recommended 10