Multi-cue based tracking

Neurocomputing

Volumn 131, Issue , 2014, Pages 227-236

  Wang, Qi ^a   Fang, Jianwu ^b   Yuan, Yuan ^b  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

Author keywords

Computer vision; Depth; Kinect; Optical flow; Tracking

Indexed keywords

EID: 84894050503     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.10.021     Document Type: Article

References (43)

1. Yang H., Shao L., Zheng F., Wang L., Song Z. Recent advances and trends in visual tracking. a review. Neurocomputing 2011, 74(18):3823-3831.
2. Z. Liu, H. Shen, G. Feng, D. Hu, Tracking objects using shape context matching. Neurocomputing 83 (2012) 47-55.
3. Wang Q., Chen F., Xu W. Adaptive multi-cue tracking by online appearance learning. Neurocomputing 2011, 74(6):1035-1045.
4. Yuan Y., Fang J., Wang Q. Robust superpixel tracking via depth fusion. IEEE Trans. Circuits Syst. Video Technol. 2013, PP(99):1.
5. W. He, T. Yamashita, H. Lu, S. Lao, SURF tracking, in: ICCV, 2009, pp. 1586-1592.
6. Miao Q., Wang G., Shi C., Lin X., Ruan Z. A new framework for on-line object tracking based on SURF. Pattern Recognit. Lett. 2011, 32(13):1564-1571.
7. Zhou H., Yuan Y., Shi C. Object tracking using SIFT features and mean shift. Comput. Vis. Image Underst. 2009, 113(3):345-352.
8. A. Adam, E. Rivlin, I. Shimshoni, Robust fragments-based tracking using the integral histogram, in: CVPR, 2006, pp. 798-805.
9. Mei X., Ling H. Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(11):2259-2272.
10. Comaniciu D., Meer P. Mean shift. a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24(5):603-619.
11. Black M.J., Jepson A.D. Eigentracking. robust matching and tracking of articulated objects using a view-based representation. Int. J. Comput. Vis. 1998, 26(1):63-84.
12. Babenko B., Yang M.H., Belongie S. Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33(8):1619-1632.
13. Kalal Z., Mikolajczyk K., Matas J. Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 34(7):1409-1422.
14. Avidan S. Support vector tracking. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26(8):1064-1072.
15. Senst T., Eiselein V., Sikora T. Robust local optical flow for feature tracking. IEEE Trans. Circuits Syst. Video Technol. 2012, 22(9):1377-1387.
16. Fan J., Xu W., Wu Y., Gong Y. Human tracking using convolutional neural networks. IEEE Trans. Neural Netw. 2010, 21(10):1610-1623.
17. Bhandarkar S.M., Luo X. Integrated detection and tracking of multiple faces using particle filtering and optical flow-based elastic matching. Comput. Vis. Image Underst. 2009, 113(6):708-725.
18. Shin J., Kim S., Kang S., Lee S., Paik J.K., Abidi B.R., et al. Optical flow-based real-time object tracking using non-prior training active feature model. Real-Time Imaging 2005, 11(3):204-218.
19. J. Shi, C. Tomasi, Good features to track, in: CVPR, 1994, pp. 593-600.
20. Ross D.A., Lim J., Lin R.S., Yang M.H. Incremental learning for robust visual tracking. Int. J. Comput. Vis. 2008, 77(1-3):125-141.
21. S. Birchfield, Elliptical head tracking using intensity gradients and color histograms, in: CVPR, 1998, pp. 232-237.
22. Wang Q., Yuan Y., Yan P., Li X. Saliency detection by multiple-instance learning. IEEE Trans. Cybern. 2013, 43(2):660-672.
23. Wang Q., Yuan Y., Yan P. Visual saliency by selective contrast. IEEE Trans. Circuits Syst. Video Technol. 2013, 23(7):1150-1155.
24. Wang Q., Yan P., Yuan Y., Li X. Multi-spectral saliency detection. Pattern Recognit. Lett. 2013, 34(1):34-41.
25. P. Michel, J.E. Chestnutt, S. Kagami, K. Nishiwaki, J.J. Kuffner, T. Kanade, GPU-accelerated real-time 3D tracking for humanoid locomotion and stair climbing, in: IROS, 2007, pp. 463-469.
26. B. Stenger, P.R.S. Mendonça, R. Cipolla, Model-based 3D tracking of an articulated hand, in: CVPR, 2001, pp. 310-315.
27. Li Y., Hilton A., Illingworth J. A relaxation algorithm for real-time multiple view 3d-tracking. Image Vis. Comput. 2002, 20(12):841-859.
28. M.H. Tu, C.M. Huang, L.C. Fu, Online 3D tracking of human arms with a single camera, in: ICRA, 2012, pp. 1378-1383.
29. R. Li, M.H. Yang, S. Sclaroff, T.P. Tian, Monocular tracking of 3D human motion with a coordinated mixture of factor analyzers, in: ECCV, 2006, pp. 137-150.
30. A. Tyagi, M.A. Keck, J.W. Davis, G. Potamianos, Kernel-based 3D tracking, in: CVPR, 2007.
31. Ess A., Leibe B., Schindler K., Gool L.J.V. Robust multiperson tracking from a mobile platform. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31(10):1831-1846.
32. S.B. Göktürk, C. Tomasi, 3D head tracking based on recognition and interpolation using a time-of-flight depth sensor, in: CVPR, 2004, pp. 211-217.
33. V. Frati, D. Prattichizzo, Using kinect for hand tracking and rendering in wearable haptics, in: World Haptics, 2011, pp. 317-321.
34. I. Oikonomidis, N. Kyriazis, A. Argyros, Efficient model-based 3D tracking of hand articulations using kinect, in: BMVC, 2011, pp. 101.1-101.11.
35. R.L. Hu, A. Hartfiel, J. Tung, A. Fakih, J. Hoey, P. Poupart, 3D pose tracking of walker users' lower limb with a structured-light camera on a moving platform, in: CVPRW, 2011, pp. 29-36.
36. Zhang Z. Microsoft kinect sensor and its effect. IEEE Trans. Multimed. 2012, 19(2):4-10.
37. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, in: ECCV, 2004, pp. 25-36.
38. Bruhn A., Weickert J., Schnörr C. Lucas/Kanade meets Horn/Schunck. combining local and global optic flow methods. Int. J. Comput. Vis. 2005, 61(3):211-231.
39. F.M. Porikli, Integral histogram: a fast way to extract histograms in cartesian spaces, in: CVPR, 2005, pp. 829-836.
40. H. Grabner, H. Bischof, On-line boosting and vision, in: CVPR, 2006, pp. 260-267.
41. H. Grabner, C. Leistner, H. Bischof, Semi-supervised on-line boosting for robust tracking, in: ECCV, 2008, pp. 234-247.
42. X. Jia, H. Lu, M.H. Yang, Visual tracking via adaptive structural local sparse appearance model, in: CVPR, 2012, pp. 1822-1829.
43. C. Liu, Beyond pixels: exploring new representations and applications for motion analysis (Ph.D. thesis), Massachusetts Institute of Technology, 2009.