基于特定目标提议框的自适应跟踪算法

doi:10.19678/j.issn.1000-3428.0052854

摘要/Abstract

摘要： 目前多数跟踪算法采用尺度遍历穷搜索策略应对目标的尺度变化，其跟踪性能和效率不佳。针对此问题，基于特定目标提议框提出一种自适应跟踪算法。对目标提议框生成算法进行改进，融入跟踪目标的尺度和位置信息，得到特定目标提议框并获取其特征。为确保跟踪的连续性，将自适应支持向量机作为跟踪模型，对特定目标提议框进行评分，得到目标位置。对均匀采样样本和特定目标提议框正负样本分类，进行模型更新。在OTB100数据库上进行对比实验，结果表明，与CNN-SVM、DeepSRDCF等算法相比，该算法能较好地适应目标的尺度变化和形变，有效提高跟踪效率。

关键词: 特定目标提议框, 自适应跟踪, 视觉目标跟踪, 深度学习, 支持向量机

Abstract: Currently,most tracking algorithms use scale-traversing poor search strategy to cope with the scale changes of targets,but the tracking performance and efficiency are not satisfying.Therefore,we propose an adaptive tracking algorithm base on specific object proposal.First,we adopt the scale and position information of the tracking target into the object proposal generation algorithm,so as to obtain the specific object proposal and its features.Then,to ensure the tracking continuity,we use the adaptive Support Vector Machine(SVM) as tracking model to score the specific object proposal,thus obtaining the target.Finally,we update the model by classifying the positive and negative samples of specific object proposal and the uniformly-sampled samples.Comparative experiments on OTB100 dataset show that compared with CNN-SVM and DeepSRDCF algorithms,the proposed algorithm can better adapt to the scale changes and deformation of the target,and improve tracking efficiency.

Key words: specific object proposal, adaptive tracking, visual target tracking, deep learning, Support Vector Machine(SVM)

中图分类号:

TP391

胡畔, 乔林, 徐立波, 于元旗, 韩永辉. 基于特定目标提议框的自适应跟踪算法[J]. 计算机工程, 2019, 45(11): 269-274.

HU Pan, QIAO Lin, XU Libo, YU Yuanqi, HAN Yonghui. Adaptive Tracking Algorithm Based on Specific Object Proposal[J]. Computer Engineering, 2019, 45(11): 269-274.

https://www.ecice06.com/CN/Y2019/V45/I11/269

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参考文献

[1] 管皓,薛向阳,安志勇.深度学习在视频目标跟踪中的应用进展与展望[J].自动化学报,2016,42(6):834-847.
[2] ZITNICK C L,DOLLARP.Edge boxes:locating object proposals from edges[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2014:391-405.
[3] KRISTAN M,MATAS J,LEONARDIS A,et al.The visual object tracking VOT2015 challenge results[C]//Proceedings of IEEE International Conference on Computer Vision Workshop.Washington D.C.,USA:IEEE Press,2015:564-586.
[4] HUANG Dafei,LUO Lei,WEN Mei,et al.Enable scale and aspect ratio adaptability in visual tracking with detection proposals[C]//Proceedings of British Machine Vision Conference.Swansea,UK:DBLP,2015:1-12.
[5] HUA Yang,ALAHARI K,SCHMID C.Online object tracking with proposal selection[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:3092-3100.
[6] ZHU Gao,PORIKLI F,LI Hongdong.Beyond local search:tracking objects everywhere with instance-specific proposals[C]//Proceedings of IEEE Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:943-951.
[7] YANG Jun,YAN Rong,HAUPTMANN A G.Adapting SVM classifiers to data with shifted distributions[C]//Proceedings of IEEE International Conference on Data Mining Workshops.Washington D.C.,USA:IEEE Press,2007:69-76.
[8] CHENG Mingming,ZHANG Ziming,LIN Wenyan,et al.Bing:binarized normed gradients for objectness estimation at 300 fps[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:3286-3293.
[9] GHODRATI A,DIBA A,PEDERSOLI M,et al.DeepProposal:hunting objects by cascading deep convolutional layers[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:2578-2586.
[10] FAN Rongen,CHANG Kaiwei,HSIEH C J,et al.LIBLINEAR:a library for large linear classification[J].Journal of Machine Learning Research,2008,9(8):1871-1874.
[11] PLATT J.Sequential minimal optimization:a fast algorithm for training support vector machines[J].International Journal of Advanced Computer Science and Applications,1998,208(14):98-112.
[12] CHATFIELD K,SIMONYAN K,VEDALDI A,et al.Return of the devil in the details:Delving deep into convolutional nets[EB/OL].[2018-09-30].https://arxiv.gg363.site/abs/1405.3531.
[13] WU Yi,LIM J,YANG M H.Object tracking benchmark[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1834-1848.
[14] HONG S,YOU T,KWAK S,et al.Online tracking by learning discriminative saliency map with convolutional neural network[EB/OL].[2018-09-30].https://arxiv.gg363.site/abs/1502.06796.
[15] DANELLJAN M,HAGER G,SHAHBAZ K F,et al.Convolutional features for correlation filter based visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.Washington D.C.,USA:IEEE Press,2015:58-66.
[16] MA Chao,HUANG Jiabin,YANG Xiaokang,et al.Hierarchical convolutional features for visual tracking[C]//Proceedings of the IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:3074-3082.
[17] QI Yuankai,ZHANG Shengping,QIN Lei,et al.Hedged deep tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press:2016:4303-4311.
[18] DANELLJAN M,HGER G,KHAN F,et al.Accurate scale estimation for robust visual tracking[C]//Proceedings of British Machine Vision Conference.Nottingham,UK:BMVA Press,2014:1-11.
[19] HENRIQUES J F,CASEIRO R,MARTINSP,et al.High-speed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):583-596.
[20] ZHANG Jianming,MA Shuguo,SCLAROFF S.MEEM:robust tracking via multiple experts using entropy minimization[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2014:188-203.
[21] DANELLJAN M,HAGER G,SHAHBAZ K F,et al.Learning spatially regularized correlation filters for visual tracking[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:4310-4318.
[22] MA Chao,YANG Xiaokang,ZHANG Chongyang,et al.Long-term correlation tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:5388-5396.
[23] GAO J,LING H,HU W,et al.Transfer learning based visual tracking with Gaussian processes regression[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2014:188-203.

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