基于VGG-M网络模型的前方车辆跟踪*

doi:10.19562/j.chinasae.qcgc.2019.01.009

汽车工程 ›› 2019, Vol. 41 ›› Issue (1): 57-63.doi: 10.19562/j.chinasae.qcgc.2019.01.009

基于VGG-M网络模型的前方车辆跟踪^*

刘国辉¹, 张伟伟¹, 吴训成¹, 宋晓琳², 许莎¹, 温培刚¹

1.上海工程技术大学机械与汽车工程学院,上海 201600;
2.湖南大学,汽车车身先进设计制造国家重点实验室,长沙 410082

收稿日期:2017-11-23 出版日期:2019-01-25 发布日期:2019-01-25
通讯作者: 张伟伟,讲师,E-mail:zwwsues@163.com。
基金资助:
*国家自然科学基金(51675324,51575169,51805312)、上海高校青年教师培养计划(ZZGCD15102)、上海工程技术大学校启科研项目(2016-19)、第八批(2017年)“上海高校教师产学研计划”(A3-0100-17-SDJH337)、上海工程技术大学研究生科研创新项目(16KY0602)和上海市青年科技英才扬帆计划(18YF1409400)资助。

Front Vehicle Tracking Based on VGG-M Network Model

Liu Guohui¹, Zhang Weiwei¹, Wu Xuncheng¹, Song Xiaolin², Xu Sha¹, Wen Peigang¹

1.School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201600;
2.Hunan University, Laboratory of Advanced Design and Manufacturing for Vehicle Body, Changsha 410082

Received:2017-11-23 Online:2019-01-25 Published:2019-01-25

摘要/Abstract

摘要： 针对前方运动车辆复杂场景下的跟踪精度较低的问题,文中将庞大的VGG-M网络模型应用到实时跟踪中,并结合在线观测模型,实现对前方车辆稳定精准的跟踪。通过改进样本生成方案,优化网络训练集,提高了网络训练效率。采用自适应更新模型,可根据目标轮廓的高宽比、内部信息熵和跟踪的尺度置信度实时调节网络更新频率。实验结果表明,在线VGG-M跟踪模型比传统的车辆跟踪方法的性能有明显的改善。

关键词: 深度学习, 前车跟踪, 在线观测模型, 网络自适应更新模型

Abstract: Aiming at the low accuracy of front moving vehicle tracking in complex scenes, the huge VGG-M network model is applied to real-time tracking, and the online observation model is used to achieve stable and accurate tracking of front vehicles. By improving the sample generation scheme and optimizing the network training set, the efficiency of network training is enhanced. Furthermore, with adaptive update model adopted, the network update frequency can be adjusted in real time according to the aspect ratio of target profile, internal information entropy and the confidence of tracking scale. Experimental results show that the online VGG-M tracking model achieves better performance than the traditional vehicle tracking methods.

Key words: deep learning, front vehicle tracking, online observation model, network adaptive update model

刘国辉, 张伟伟, 吴训成, 宋晓琳, 许莎, 温培刚. 基于VGG-M网络模型的前方车辆跟踪^*[J]. 汽车工程, 2019, 41(1): 57-63.

Liu Guohui, Zhang Weiwei, Wu Xuncheng, Song Xiaolin, Xu Sha, Wen Peigang. Front Vehicle Tracking Based on VGG-M Network Model[J]. , 2019, 41(1): 57-63.

参考文献

[1] 鹿文浩,王生进.鲁棒的车载摄像头对向车辆检测与跟踪方法[J].清华大学学报(自然科学版),2013(11):1509-1514.
[2] LU H, JIA X, YANG M H. Visual tracking via adaptive structural local sparse appearance model[C]. Computer Vision and Pattern Recognition. IEEE,2012:1822-1829.
[3] SANTNER J, LEISTNER C, SAFFARI A, et al. PROST: parallel robust online simple tracking[C]. Computer Vision and Pattern Recognition. IEEE,2010:723-730.
[4] 顾柏园,王荣本,郭烈,等.基于机器视觉的道路上前方多车辆探测方法研究[J].汽车工程,2006,28(10):902-905.
[5] WANG N, YEUNG D Y. Learning a deep compact image representation for visual tracking[C]. International Conference on Neural Information Processing Systems. Curran Associates Inc,2013:809-817.
[6] WANG N, LI S, GUPTA A, et al. Transferring rich feature hierarchies for robust visual tracking[J]. Computer Science,2015:1-9.
[7] WANG L, OUYANG W, WANG X, et al. Visual tracking with fully convolutional networks[C]. IEEE International Conference on Computer Vision. IEEE Computer Society,2015:3119-3127.
[8] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science,2014:1-14.
[9] MA C, HUANG J B, YANG X, et al. Hierarchical convolutional features for visual tracking[C]. IEEE International Conference on Computer Vision. IEEE Computer Society,2015:3074-3082.
[10] NAM H, HAN B. Learning multi-domain convolutional neural networks for visual tracking[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016:4293-4302.
[11] HAN B, SIM J, ADAM H. BranchOut: regularization for online ensemble tracking with convolutional neural networks[C]. IEEE Conference on Computer Vision and Pattern Recognition. IEEE,2017:521-530.
[12] CHATFIELD K, SIMONYAN K, VEDALDI A, et al. Return of the devil in the details: delving deep into convolutional nets[J]. Computer Science,2014:1-11.
[13] RUSSAKOVSKY O, DENG J, SU H, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision,2015,115(3):211-252.
[14] 贾静平,覃亦华.基于深度学习的视觉跟踪算法研究综述[J].计算机科学,2017,44(s1):19-23.
[15] YIN C, KAN R, GU G, et al. Moving object detection based on improved single gaussian background model[J]. Chinese Journal of Lasers,2014,41(11):239-247.
[16] LI H, WU H, LIN S, et al. Coupling deep correlation filter and online discriminative learning for visual object tracking[J]. Journal of Computational and Applied Mathematics,2017,329:91-201.
[17] CRAMMER K, DEKEL O, KESHET J, et al. Online passive-aggressive algorithms[J]. Journal of Machine Learning Research,2006,7(3):551-585.
[18] DANELLJAN M, BHAT G, KHAN F S, et al. ECO: efficient convolution operators for tracking[C]. Computer Vision and Pattern Recognition (CVPR),2017 IEEE Conference on. IEEE,2017:6931-6939.

基于VGG-M网络模型的前方车辆跟踪^*

Front Vehicle Tracking Based on VGG-M Network Model

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