Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 19-23.doi: 10.11896/j.issn.1002-137X.2017.6A.004

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Survey on Visual Tracking Algorithms Based on Deep Learning Technologies

JIA Jing-ping and QIN Yi-hua   

  • Online:2017-12-01 Published:2018-12-01

Abstract: Visual tracking is a fundamental subject in the field of computer vision.Classical tracking methods are not good at handling the problems of the complex background,such as illumination variation,great change of the target size and posture change greatly or occlusion and so on.Meanwhile,the introduction of deep learning technologies opens a new way of visual tracking study.There are a few research literature on visual tracking based on deep learning relatively,both in China and abroad at present.In order to attract more researchers in the field of visual tracking to explore and discuss deep learning,and to promote the research of visual tracking algorithm,this overview briefly reviewed the research status of visual tracking and deep learning.Then we focused on the related literatures about algorithms of visual tracking based on deep learning,and discussed their advantages and disadvantages.Finally,we proposed the direction of further research and the prospect of visual tracking algorithm based on deep learning.

Key words: Computer vision,Visual tracking,Deep learning

[1] 马颂德.计算机视觉[M].科学出版社,1998.
[2] 侯志强,韩崇昭.视觉跟踪技术综述[J].自动化学报,2006,32(4):603-617.
[3] HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets [J].Neural Computation,2006,18(7):1527-1554.
[4] COMANICIU D,RAMESH V,MEER P.Kernel-Based Object Tracking [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2003,25(5):564-575.
[5] YANG M H.Visual tracking via adaptive structural local sparse appearance model[C]∥Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition.2012:1822-1829.
[6] ZHANG Z,WONG K H.Pyramid-Based Visual Tracking Using Sparsity Represented Mean Transform[C]∥Proceedings of the Computer Vision and Pattern Recognition.2014:1226-1233.
[7] HAN B,COMANICIU D,ZHU Y,et al.Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2008,30(7):1186-1197.
[8] JING L.Incremental Learning for Robust Visual Tracking [J].International Journal of Computer Vision,2008,77(1-3):125-141.
[9] JEPSON A D,FLEET D J,EL-MARAGHI T R.Robust online appearance models for visual tracking[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision & Pattern Recognition.2003.
[10] VARAS D,MARQUES F.Region-Based Particle Filter for Vi-deo Object Segmentation[C]∥Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2014:3470-3477.
[11] AVIDAN S.Support vector tracking [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2004,26(8):1064-1072.
[12] BAI Y.Robust tracking via weakly supervised ranking SVM[C]∥Proceedings of the IEEE Conference on Computer Vision & Pattern Recognition.2012:1854-1861.
[13] SANTNER J,LEISTNER C,SAFFARI A,et al.PROST:Parallel robust online simple tracking[C]∥CVPR 2010.2010.
[14] BABENKO B,YANG M H,BELONGIE S.Robust ObjectTracking with Online Multiple Instance Learning [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2010,33(8):1619-1632.
[15] GRABNER H,BISCHOF H.On-line Boosting and Vision[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision & Pattern Recognition.2006:260-267.
[16] GRABNER H,LEISTNER C,BISCHOF H.Semi-supervised On-Line Boosting for Robust Tracking[C]∥Proceedings of the European Conference on Computer Vision.2008:234-247.
[17] BENGIO Y,COURVILLE A,VINCENT P.RepresentationLearning:A Review and New Perspectives [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2013,35(8):1798-1828.
[18] FUKUSHIMA K.NEOCOGNITRON:A self-organizing neural network model for a mechanism of pattern recognition unaffec-ted by shift in position [J].Biological Cybernetics,1980,36(36):193-202.
[19] LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-Based Lear-ning Applied to Document Recognition [C]∥Proceedings of the IEEE.1998:2278-2324.
[20] LECUN Y,BOSER B,DENKER J S,et al.Backpropagation Applied to Handwritten Zip Code Recognition [J].Neural Computation,1989,1(4):541-551.
[21] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet Classification with Deep Convolutional Neural Networks [J].Advances in Neural Information Processing Systems,2012,25(2):2012.
[22] SUN Y,WANG X,TANG X.Deeply learned face representations are sparse,selective,and robust [J].arXiv:1412.1265,4.
[23] GIRSHICH R,DONAHUE J,DARRELL T,et al.Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation [C]∥CVPR.2014:580-587.
[24] FAN J,XU W,WU Y,et al.Human tracking using convolutio-nal neural networks[J].IEEE Transactions on Neural Networks,2010,21(10):1610-1623.
[25] HONG S,YOU T,KWAK S,et al.Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network [C]∥Proceedings of The 32nd International Confernece on Machine Learning.2015:597-606.
[26] LI H,LI Y,PORIKLI F.Robust Online Visual Tracking with a Single Convolutional Neural Network [M].Springer International Publishing,2014.
[27] MA C,HUANG J,YANG X,et al.Hierarchical Convolutional Features for Visual Tracking[C]∥Proceedings of the IEEE International Conference on Computer Vision.2016:3074-3082.
[28] NAM H,HAN B.Learning Multi-Domain Convolutional Neural Networks for Visual Tracking [J].arXiv:1510.07945.
[29] WANG L,OUYANG W,WANG X,et al.Visual Tracking with Fully Convolutional Networks[C]∥Proceedings of the IEEE International Conference on Computer Vision.2015.
[30] ZHANG K,LIU Q,WU Y,et al.Robust Visual Tracking via Convolutional Networks Without Training [J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2016,25(4):1779-1792.
[31] WANG N,YEUNG D Y.Learning a deep compact image representation for visual tracking [C]∥Advances in Neural Information Processing Systems.2013:809-817.
[32] ZHOU X,XIE L,ZHANG P,et al.An ensemble of deep neural networks for object tracking[C]∥Proceedings of the IEEE International Conference on Image Processing.2014:843-847.
[33] SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition [J].arXiv:1409.1556.
[34] OUYANG W,LUO P,ZENG X,et al.DeepID-Net:multi-stage and deformable deep convolutional neural networks for object detection [J].arXiv:1409.3505 .
[35] SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]∥Proceedings of the Computer Vision and Pattern Recognition.2015:1-9.
[36] CHATFIELD K,SIMONYAN K,VEDALDI A,et al.Return of the Devil in the Details:Delving Deep into Convolutional Nets [J].arXiv:1405.3531.

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