目标跟踪算法综述
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摘要
目标跟踪一直以来都是计算机视觉领域的关键问题,最近随着人工智能技术的飞速发展,运动目标跟踪问题得到了越来越多的关注.本文对主流目标跟踪算法进行了综述,首先,介绍了目标跟踪中常见的问题,并由时间顺序对目标跟踪算法进行了分类:早期的经典跟踪算法、基于核相关滤波的跟踪算法以及基于深度学习的跟踪算法.接下来,对每一类中经典的跟踪算法的原始版本和各种改进版本做了介绍、分析以及比较.最后,使用OTB-2013数据集对目标跟踪算法进行测试,并对结果进行分析,得出了以下结论:1)相比于光流法、Kalman、Meanshift等传统算法,相关滤波类算法跟踪速度更快,深度学习类方法精度高. 2)具有多特征融合以及深度特征的追踪器在跟踪精度方面的效果更好. 3)使用强大的分类器是实现良好跟踪的基础. 4)尺度的自适应以及模型的更新机制也影响着跟踪的精度.
Object tracking has always been a key issue in the computer vision field. Recently, with the rapid development of artificial intelligence, the issue of moving object tracking has attracted more and more attention. This paper reviews the main object tracking algorithms. Firstly, we introduce the common problems in object tracking and classify the object tracking algorithms into three groups: early object tracking algorithms, kernelized correlation filters(KCF) object tracking algorithms, deep learning object tracking algorithms. Then, according to the three groups, we introduce and analyze many famous object tracking algorithms and their following improved versions. Finally, we analyze and compare the performance from many object tracking algorithms using dataset OTB-2013, and conclude that: 1) Compared with the optical flow method, Kalman, meanshift and other early algorithms, the tracking speed of KCF-based algorithms are faster and the deep learning based algorithms have higher accuracy. 2) The tracking algorithms with multiple feature fusion or deep features have higher tracking accuracy. 3) Powerful classifiers are the basis for good tracking results. 4)Scale adaptation and updating mechanism also affect tracking accuracy.
Object tracking has always been a key issue in the computer vision field. Recently, with the rapid development of artificial intelligence, the issue of moving object tracking has attracted more and more attention. This paper reviews the main object tracking algorithms. Firstly, we introduce the common problems in object tracking and classify the object tracking algorithms into three groups: early object tracking algorithms, kernelized correlation filters(KCF) object tracking algorithms, deep learning object tracking algorithms. Then, according to the three groups, we introduce and analyze many famous object tracking algorithms and their following improved versions. Finally, we analyze and compare the performance from many object tracking algorithms using dataset OTB-2013, and conclude that: 1) Compared with the optical flow method, Kalman, meanshift and other early algorithms, the tracking speed of KCF-based algorithms are faster and the deep learning based algorithms have higher accuracy. 2) The tracking algorithms with multiple feature fusion or deep features have higher tracking accuracy. 3) Powerful classifiers are the basis for good tracking results. 4)Scale adaptation and updating mechanism also affect tracking accuracy.
引文
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3 Choi J Y, Sung K S, Yang Y K. Multiple vehicles detec-tion and tracking based on scale-invariant feature transform.In:Proceedings of the 2007 IEEE Intelligent Transportation Systems Conference. Seattle, WA, USA:IEEE, 2007.528-533
4 Bay H, Tuytelaars T, Van Gool L. SURF:speeded up robust features. In:Proceeding of Computer Vision-ECCV2006. Lecture Notes in Computer Science, vol. 3951. Berlin,Heidelberg:Germany Springer, 2006. 404-417
5 Nguyen M H, Wunsche B, Delmas P, Lutteroth C. Modelling of 3D objects using unconstrained and uncalibrated images taken with a handheld camera. In:Proceedings of the Computer Vision, Imaging and Computer Graphics. Communications in Computer and Information Science, vol. 274.Berlin, Heidelberg:Springer, 2011. 86-101
6 Kass M, Witkin A, Terzopoulos D. Snakes:active contour models. International Journal of Computer Vision, 1988,1(4):321-331
7 Welch G, Bishop G. An Introduction to the Kalman Filter. Chapel Hill, NC, USA:University of North Carolina at Chapel Hill, 2001.
8 Nummiaro K, Koller-Meier E, Van Gool L. An adaptive color-based particle filter. Image and Vision Computing,2003, 21(1):99-110
9 Du K, Ju Y F, Jin Y L, Li G, Li Y Y, Qian S L. Object tracking based on improved MeanShift and SIFT. In:Proceedings of the 2nd International Conference on Consumer Electronics, Communications and Networks. Yichang, China:IEEE,2012. 2716-2719
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11 Exner D, Bruns E, Kurz D, Grundhofer A, Bimber O. Fast and robust CAMShift tracking. In:Proceedings of the 2010IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA:IEEE,2010. 9-16
12 Bolme D S, Beveridge J R, Draper B A, Lui Y M. Visual object tracking using adaptive correlation filters. In:Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco,CA, USA:IEEE, 2010. 2544-2550
13 Henriques J F, Caseiro R, Martins P, Batista J. Exploiting the circulant structure of tracking-by-detection with kernels.In:Proceedings of Computer Vision. Lecture Notes in Computer Science, vol. 7575. Berlin, Heidelberg:Springer, 2012.702-715
14 Henriques J F, Caseiro R, Martins P, Batista J. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,37(3):583-596
15 Galoogahi H K, Fagg A, Lucey S. Learning backgroundaware correlation filters for visual tracking. In:Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 2017. 1144-1152
16 Li Y, Zhu J. A scale adaptive kernel correlation filter tracker with feature integration. In:Proceedings of Computer Vision, Lecture Notes in Computer Science, vol. 8926. Zurich,Switzerland:Springer, 2014. 254-265
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20 Kristan M, Leonardis A, Matas J, Felsberg M, Pflugfelder R, Zajc L C, et al. The visual object tracking VOT2017challenge results. In:Proceedings of the 2017 IEEE International Conference on Computer Vision Workshop. Venice,Italy:IEEE, 2017. 1949-1972
21 Zhang S P, Yao H X, Sun X, Lu X S. Sparse coding based visual tracking:review and experimental comparison. Pattern Recognition, 2013, 46(7):1772-1788
22 Gong H F, Sim J, Likhachev M, Shi J B. Multi-hypothesis motion planning for visual object tracking. In:Proceedings of the 2011 International Conference on Computer Vision.Barcelona, Spain:IEEE, 2011. 619-626
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24 Xu L, Jia J Y, Matsushita Y. Motion detail preserving optical flow estimation. In:Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA:IEEE, 2010.1293-1300
25 Choi J, Kwon J, Lee K M. Visual tracking by reinforced decision making. Advances in Visual Computing, 2014. 270-280
26 Babenko B, Yang M H, Belongie S. Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8):1619-1632
27 Kalal Z, Mikolajczyk K, Matas J. Tracking-learningdetection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7):1409-1422
28 Avidan S. Support vector tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(8):1064-1072
29 Hare S, Saffari A, Torr P H S. Struck:structured output tracking with kernels. In:Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona,Spain:IEEE, 2011. 263-270
30 Saffari A, Leistner C, Santner J, Godec M, Bischof H. Online random forests. In:Proceedings of the IEEE 12th International Conference on Computer Vision Workshops. Kyoto,Japan:IEEE, 2009. 1393-1400
31 Babenko B, Yang M H, Belongie S. Visual tracking with online multiple instance learning. In:Proceedings of the 2009IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL:IEEE, 2009. 983-990
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