Details of Research Outputs

TitleA Novel Hybrid Level Set Model for Non-Rigid Object Contour Tracking
Author (Name in English or Pinyin)
Cai, Qing1,2,3; Liu, Huiying4; Qian, Yiming5; Zhou, Sanping6; Wang, Jinjun6; Yang, Yee-Hong7
Date Issued2022
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
DOI10.1109/TIP.2021.3112051
Indexed BySCIE
Firstlevel Discipline计算机科学技术
Education discipline科技类
Published range国外学术期刊
Volume Issue Pages卷: 31 页: 15-29
References
[1] A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey, " ACM Comput. Surv., vol. 38, no. 4, p. 13, 2006.
[2] P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, "Color-based probabilistic tracking, " in Proc. Eur. Conf. Comput. Vis. Berlin, Germany: Springer, 2002, pp. 661-675.
[3] A. Adam, E. Rivlin, and I. Shimshoni, "Robust fragments-based tracking using the integral histogram, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., vol. 1, Jun. 2006, pp. 798-805.
[4] J. Ning, L. Zhang, D. Zhang, and C. Wu, "Robust object tracking using joint color-texture histogram, " Int. J. Pattern Recognit. Artif. Intell., vol. 23, no. 7, pp. 1245-1263, Nov. 2009.
[5] B. Babenko, M.-H. Yang, and S. Belongie, "Robust object tracking with online multiple instance learning, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1619-1632, Aug. 2011.
[6] J. S. Supancic, III, and D. Ramanan, "Self-paced learning for long-term tracking, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 2379-2386.
[7] M. Kristan et al., "The visual object tracking VOT2013 challenge results, " in Proc. IEEE Int. Conf. Comput. Vis. Workshops, Dec. 2013, pp. 98-111.
[8] H. Grabner, C. Leistner, and H. Bischof, "Semi-supervised on-line boosting for robust tracking, " in Proc. Eur. Conf. Comput. Vis., 2008, pp. 234-247.
[9] S. Hare, A. Saffari, and P. H. S. Torr, "Struck: Structured output tracking with kernels, " in Proc. Int. Conf. Comput. Vis., Nov. 2011, pp. 263-270.
[10] J. Choi, H. J. Chang, J. Jeong, Y. Demiris, and J. Y. Choi, "Visual tracking using attention-modulated disintegration and integration, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 4321-4330.
[11] M. Danelljan, A. Robinson, F. S. Khan, and M. Felsberg, "Beyond correlation filters: Learning continuous convolution operators for visual tracking, " in Proc. Eur. Conf. Comput. Vis. Cham, Switzerland: Springer, 2016, pp. 472-488.
[12] H. Nam, M. Baek, and B. Han, "Modeling and propagating CNNs in a tree structure for visual tracking, " 2016, arXiv: 1608. 07242. [Online]. Available: http://arxiv. org/abs/1608. 07242
[13] L. Bertinetto, J. Valmadre, S. Golodetz, O. Miksik, and P. H. S. Torr, "Staple: Complementary learners for real-time tracking, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 1401-1409.
[14] C. Ma, J.-B. Huang, X. Yang, and M.-H. Yang, "Robust visual tracking via hierarchical convolutional features, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 11, pp. 2709-2723, Nov. 2019.
[15] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 5, pp. 564-577, May 2003.
[16] Z. Zivkovic and B. Kröse, "An EM-like algorithm for color-histogrambased object tracking, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2004, pp. 798-803.
[17] Z. H. Khan, I. Y. H. Gu, and A. G. Backhouse, "Robust visual object tracking using multi-mode anisotropic mean shift and particle filters, " IEEE Trans. Circuits Syst. Video Technol., vol. 21, no. 1, pp. 74-87, Jan. 2011.
[18] J. Ning, L. Zhang, D. Zhang, and C. Wu, "Scale and orientation adaptive mean shift tracking, " IET Comput. Vis., vol. 6, no. 1, pp. 52-61, Jan. 2012.
[19] M. Danelljan, G. Häger, F. S. Khan, and M. Felsberg, "Discriminative scale space tracking, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 8, pp. 1561-1575, Aug. 2017.
[20] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, " IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174-188, Feb. 2002.
[21] Y. Rathi, N. Vaswani, A. Tannenbaum, and A. Yezzi, "Tracking deforming objects using particle filtering for geometric active contours, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 8, pp. 1470-1475, Jun. 2007.
[22] X. Sun and H. Yao, "A refined particle filter based on determined level set model for robust contour tracking, " Mach. Vis. Appl., vol. 25, no. 7, pp. 1727-1736, Oct. 2014.
[23] M. Danelljan, G. Häger, F. S. Khan, and M. Felsberg, "Accurate scale estimation for robust visual tracking, " in Proc. Brit. Mach. Vis. Conf., 2014, pp. 1-11.
[24] Z. Liu, Z. Lian, and Y. Li, "A novel adaptive kernel correlation filter tracker with multiple feature integration, " in Proc. IEEE Int. Conf. Image Process. (ICIP), Sep. 2017, pp. 254-265.
[25] J. F. Henriques, R. Caseiro, P. Martins, and J. Batista, "High-speed tracking with kernelized correlation filters, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 3, pp. 583-596, Mar. 2014.
[26] X. Zhou, X. Li, and W. Hu, "Learning a superpixel-driven speed function for level set tracking, " IEEE Trans. Cybern., vol. 46, no. 7, pp. 1498-1510, Jul. 2016.
[27] T. F. Chan and L. A. Vese, "Active contours without edges, " IEEE Trans. Image Process., vol. 10, no. 2, pp. 266-277, Feb. 2001.
[28] C. Li, C. Xu, C. Gui, and M. D. Fox, "Distance regularized level set evolution and its application to image segmentation, " IEEE Trans. Image Process., vol. 19, no. 12, pp. 3243-3254, Aug. 2010.
[29] K. Zhang, L. Zhang, K.-M. Lam, and D. Zhang, "A level set approach to image segmentation with intensity inhomogeneity, " IEEE Trans. Cybern., vol. 46, no. 2, pp. 546-557, Feb. 2016.
[30] S. Zhou, J. Wang, M. Zhang, Q. Cai, and Y. Gong, "Correntropy-based level set method for medical image segmentation and bias correction, " Neurocomputing, vol. 234, pp. 216-229, Apr. 2017.
[31] H. Min, W. Jia, Y. Zhao, W. Zuo, Y. Luo, and H. Ling, "LATE: A level-set method based on local approximation of Taylor expansion for segmenting intensity inhomogeneous images, " IEEE Trans. Image Process., vol. 27, no. 10, pp. 5016-5031, Oct. 2018.
[32] H. Ali, L. Rada, and N. Badshah, "Image segmentation for intensity inhomogeneity in presence of high noise, " IEEE Trans. Image Process., vol. 27, no. 8, pp. 3729-3738, Aug. 2018.
[33] Q. Cai, H. Liu, S. Zhou, J. Sun, and J. Li, "An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation, " Pattern Recognit., vol. 82, pp. 79-93, Oct. 2018.
[34] Q. Cai, H. Liu, Y. Qian, S. Zhou, X. Duan, and Y.-H. Yang, "Saliencyguided level set model for automatic object segmentation, " Pattern Recognit., vol. 93, pp. 147-163, Sep. 2019.
[35] H. Zhang, L. Tang, and C. He, "A variational level set model for multiscale image segmentation, " Inf. Sci., vol. 493, pp. 152-175, Aug. 2019.
[36] W. Zhang, X. Wang, W. You, J. Chen, P. Dai, and P. Zhang, "RESLS: Region and edge synergetic level set framework for image segmentation, " IEEE Trans. Image Process., vol. 29, pp. 57-71, 2020.
[37] N. Paragios and R. Deriche, "Geodesic active contours and level sets for the detection and tracking of moving objects, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 3, pp. 266-280, Mar. 2000.
[38] D. Freedman and T. Zhang, "Active contours for tracking distributions, " IEEE Trans. Image Process., vol. 13, no. 4, pp. 518-526, Apr. 2004.
[39] T. Zhang and D. Freedman, "Improving performance of distribution tracking through background mismatch, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 2, pp. 282-287, Feb. 2005.
[40] D. Cremers, "Dynamical statistical shape priors for level set-based tracking, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 8, pp. 1262-1273, Aug. 2006.
[41] F. Bunyak, K. Palaniappan, S. K. Nath, and G. Seetharaman, "Flux tensor constrained geodesic active contours with sensor fusion for persistent object tracking, " J. Multimedia, vol. 2, no. 4, pp. 20-33, Aug. 2007.
[42] P. Chockalingam, N. Pradeep, and S. Birchfield, "Adaptive fragmentsbased tracking of non-rigid objects using level sets, " in Proc. IEEE 12th Int. Conf. Comput. Vis., Sep. 2009, pp. 1530-1537.
[43] C. Bibby and I. Reid, "Real-time tracking of multiple occluding objects using level sets, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 1307-1314.
[44] W. Hu, X. Zhou, W. Li, W. Luo, X. Zhang, and S. Maybank, "Active contour-based visual tracking by integrating colors, shapes, and motions, " IEEE Trans. Image Process., vol. 22, no. 5, pp. 1778-1792, May 2013.
[45] N. Paragios and R. Deriche, "Geodesic active regions and level set methods for motion estimation and tracking, " Comput. Vis. Image Understand., vol. 97, no. 3, pp. 259-282, 2005.
[46] Q. Chen, Q.-S. Sun, P. A. Heng, and D.-S. Xia, "Two-stage object tracking method based on kernel and active contour, " IEEE Trans. Circuits Syst. Video Technol., vol. 20, no. 4, pp. 605-609, Apr. 2010.
[47] X. Sun, H. Yao, and S. Zhang, "A novel supervised level set method for non-rigid object tracking, " in Proc. CVPR, Jun. 2011, pp. 3393-3400.
[48] J. Ning, L. Zhang, D. Zhang, and W. Yu, "Joint registration and active contour segmentation for object tracking, " IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 9, pp. 1589-1597, Sep. 2013.
[49] X. Sun, H. Yao, S. Zhang, and D. Li, "Non-rigid object contour tracking via a novel supervised level set model, " IEEE Trans. Image Process., vol. 24, no. 11, pp. 3386-3399, Nov. 2015.
[50] X. Ning and L. Liu, "Level set based online visual tracking via convolutional neural network, " in Proc. Int. Conf. Neural Inf. Process. Cham, Switzerland: Springer, 2017, pp. 280-290.
[51] X. Sun, N.-M. Cheung, H. Yao, and Y. Guo, "Non-rigid object tracking via deformable patches using shape-preserved KCF and level sets, " in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2017, pp. 5495-5503.
[52] J. Lie, M. Lysaker, and X.-C. Tai, "A binary level set model and some applications to Mumford-Shah image segmentation, " IEEE Trans. Image Process., vol. 15, no. 5, pp. 1171-1181, May 2006.
[53] R. Vert and J.-P. Vert, "Consistency and convergence rates of oneclass SVMs and related algorithms, " J. Mach. Learn. Res., vol. 7, pp. 817-854, May 2006.
[54] V. Gómez-Verdejo, J. Arenas-Garciá, M. Lazaro-Gredilla, and Á. Navia-Vazquez, "Adaptive one-class support vector machine, " IEEE Trans. Signal Process., vol. 59, no. 6, pp. 2975-2981, Jun. 2011.
[55] S. Wang, Q. Liu, E. Zhu, F. Porikli, and J. Yin, "Hyperparameter selection of one-class support vector machine by self-adaptive data shifting, " Pattern Recognit., vol. 74, pp. 198-211, Feb. 2018.
[56] M. Gong, Y. Qian, and L. Cheng, "Integrated foreground segmentation and boundary matting for live videos, " IEEE Trans. Image Process., vol. 24, no. 4, pp. 1356-1370, Apr. 2015.
[57] K. Q. Weinberger and L. K. Saul, "Distance metric learning for large margin nearest neighbor classification, " J. Mach. Learn. Res., vol. 10, pp. 207-244, Feb. 2009.
[58] S. Hadfield, K. Lebeda, and R. Bowden, "The visual object tracking VOT2014 challenge results, " in Proc. Eur. Conf. Comput. Vis. Workshops, 2014, pp. 191-217.
[59] S. J. Hadfield, R. Bowden, and K. Lebeda, "The visual object tracking VOT2016 challenge results, " in Proc. Eur. Conf. Comput. Vis. Workshops, in Lecture Notes in Computer Science, vol. 9914, 2016, pp. 777-823.
[60] F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, "A benchmark dataset and evaluation methodology for video object segmentation, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2016, pp. 724-732.
[61] S. Agarwal, A. Awan, and D. Roth, "Learning to detect objects in images via a sparse, part-based representation, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 11, pp. 1475-1490, Nov. 2004.
[62] M. Kristan et al., "A novel performance evaluation methodology for single-target trackers, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 11, pp. 2137-2155, Nov. 2016.
[63] Z. Cai, L. Wen, Z. Lei, N. Vasconcelos, and S. Z. Li, "Robust deformable and occluded object tracking with dynamic graph, " IEEE Trans. Image Process., vol. 23, no. 12, pp. 5497-5509, Dec. 2014.
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttps://irepository.cuhk.edu.cn/handle/3EPUXD0A/2539
CollectionSchool of Data Science
Corresponding AuthorCai, Qing
Affiliation
1.Chinese Univ Hong Kong , Sch Data Sci, Shenzhen 518172, Guangdong, Peoples R China
2.Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230026, Anhui, Peoples R China
3.Shenzhen Res Inst Big Data, Shenzhen 518172, Guangdong, Peoples R China
4.Northwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R China
5.Univ Manitoba, Dept Comp Sci, Winnipeg, MB R3T 2N2, Canada
6.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Shaanxi, Peoples R China
7.Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2S4, Canada
Recommended Citation
GB/T 7714
Cai, Qing,Liu, Huiying,Qian, Yiminget al. A Novel Hybrid Level Set Model for Non-Rigid Object Contour Tracking[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022.
APA Cai, Qing, Liu, Huiying, Qian, Yiming, Zhou, Sanping, Wang, Jinjun, & Yang, Yee-Hong. (2022). A Novel Hybrid Level Set Model for Non-Rigid Object Contour Tracking. IEEE TRANSACTIONS ON IMAGE PROCESSING.
MLA Cai, Qing,et al."A Novel Hybrid Level Set Model for Non-Rigid Object Contour Tracking".IEEE TRANSACTIONS ON IMAGE PROCESSING (2022).
Files in This Item:
There are no files associated with this item.
Related Services
Usage statistics
Google Scholar
Similar articles in Google Scholar
[Cai, Qing]'s Articles
[Liu, Huiying]'s Articles
[Qian, Yiming]'s Articles
Baidu academic
Similar articles in Baidu academic
[Cai, Qing]'s Articles
[Liu, Huiying]'s Articles
[Qian, Yiming]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Cai, Qing]'s Articles
[Liu, Huiying]'s Articles
[Qian, Yiming]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.