Adaptive pyramid mean shift for global real-time visual tracking

Image and Vision Computing

Volumn 28, Issue 3, 2010, Pages 424-437

  Li, Shu Xiao ^a   Chang, Hong Xing ^a   Zhu, Cheng Fei ^a  

^a INSTITUTE OF AUTOMATION   (China)

Author keywords

Adaptive level; Fast mean shift; Global visual tracking; Kernel based tracking; Tracking and pointing subsystem

Indexed keywords

CAMERAS; COMPUTATIONAL COMPLEXITY; IMAGE RETRIEVAL; TARGETS; VECTOR QUANTIZATION;

ADAPTIVE PYRAMID; ANALYSIS TECHNIQUES; CAMERA MOTIONS; CAMERA ZOOMS; COARSE-TO-FINE; FAST MEAN SHIFT; GLOBAL MAXIMUM; HIGH-SPEED; KERNEL-BASED TRACKING; MEAN SHIFT; MOVING OBJECT TRACKING; NUMBER OF ITERATIONS; OBJECT SIZE; PARTIAL OCCLUSIONS; TRACKING OBJECTS; VISUAL TRACKING; WEIGHTED HISTOGRAM;

TARGET TRACKING;

EID: 70449709423     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2009.06.012     Document Type: Article

References (36)

1. S. Avidan, Support vector tracking, in: IEEE conference on Computer Vision and Pattern Recognition, vol. I, 2001, pp. 184-191.
2. Kumar R., Sawhney H., Samarasekera S., et al. Aerial video surveillance and exploitation. IEEE Third Generation Surveill. Syst. 89 10 (2001) 1518-1539
3. Guo Y., Hsu S., Sawhney H.S., et al. Robust object matching for persistent tracking with heterogeneous features. IEEE Trans. Pattern Anal. Mach. Intell. 29 5 (2007) 824-839
4. Forsyth D.A., and Ponce J. Computer Vision - A Modern Approach (2003), Pearson Education Inc.
5. Julier S., and Uhlmann J. A new extension of the Kalman filter to nonlinear systems. SPIE 3068 (1997) 182-193
6. Chen Y., Rui Y., and Huang T.S. Multicue HMM-UKF for real-time contour tracking. IEEE Trans. Pattern Anal. Mach. Intell. 28 9 (2006) 1525-1529
7. Arulampalam S., Maskell S., Gordon N., and Clapp T. A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50 2 (2002) 174-188
8. Khan Z., Balch T., and Dellaert F. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27 11 (2005) 1805-1819
9. Khan Z., Balch T., and Dellaert F. MCMC data association and sparse factorization updating for real time multi-target tracking with merged and multiple measurements. IEEE Trans. Pattern Anal. Mach. Intell. 28 12 (2006) 1960-1972
10. Xu X., and Li B. Adaptive Rao-Blackwellized particle filter and its evaluation for tracking in surveillance. IEEE Trans. Image Process. 16 3 (2007) 838-849
11. B. Georgescu, I. Shimshoni, P. Meer, Mean shift based clustering in high dimensions: a texture classification example, in: IEEE International Conference on Computer Vision, vol. 2, 2003, pp. 456-463.
12. Comaniciu D., and Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24 5 (2002) 603-619
13. R. Collins, Mean shift blob tracking through scale space, in: IEEE conference on Computer Vision and Pattern Recognition, vol. 2, 2003, pp. 234-240.
14. Comaniciu D., Ramesh V., and Meer P. Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25 5 (2003) 564-577
15. Cheng Y. Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17 8 (1995) 790-799
16. Fashing M., and Tomasi C. Mean shift is a bound optimization. IEEE Trans. Pattern Anal. Mach. Intell. 27 3 (2005) 471-474
17. Collins R., Liu Y., and Eeordeanu M. Online selection of discriminative tracking features. IEEE Trans. Pattern Anal. Mach. Intell. 27 10 (2005) 1631-1643
18. Liu T., and Chen H. Real-time tracking using trust-region methods. IEEE Trans. Pattern Anal. Mach. Intell. 26 3 (2004) 397-402
19. C. Shen, M. Brooks, A. Hengel, Fast global kernel density mode seeking with applications to localization and tracking, in: IEEE International Conference on Computer Vision, vol. 2, 2005, pp. 1516-1523.
20. Shen C., Brooks M., and Hengel A. Fast global kernel density mode seeking: applications to localization and tracking. IEEE Trans. Image Process. 16 5 (2007) 1457-1469
21. Xu C., and Sun S. An Introduction to Calculation Methods. second ed. (2002), High Education Press, Beijing, China
22. Hall P., Minnotte M.C., and Zhang C. Bump hunting with non-Gaussian kernels. Ann. Stat. 32 5 (2004) 2124-2141
23. Zhang F., Cao X.B., and Zou J.X. A new large-scale transformation algorithm of quaternion to Euler angle. Chin. J. Nanjing Univ. Sci. Technol. 24 4 (2002) 376-380
24. Liu Y.Z., and Ma X.M. Quaternion-based adaptive control of a spacecraft with reaction wheels. Chin. J. Shanghai Jiaotong Univ. 37 12 (2003) 1957-1960
25. Wang M.G., and Zhai J.Q. Design and simulation of miniature AHRs based on DSP. Chin. J. Comput. Simul. 22 8 (2005) 50-53
26. Dawoud A., Alam M.S., Bal A., and Loo C. Target tracking in infrared imagery using weighted composite reference function-based decision fusion. IEEE Trans. Image Process. 15 2 (2006) 404-410
27. Avidan S. Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29 2 (2007) 261-271
28. Sun Z.H., Bebis G., and Miller R. On-road vehicle detection: a review. IEEE Trans. Pattern Anal. Mach. Intell. 28 5 (2006) 694-711
29. C.J. Yang, R. Duraiswami, L. Davis, Fast multiple object tracking via a hierarchical particle filter, in: IEEE International Conference on Computer Vision, 2005, pp. 212-219.
30. E. Rosten, T. Drummond, Fusing points and lines for high performance tracking, in: IEEE International Conference on Computer Vision, 2005, pp. 1508-1515.
31. Wang J.Q., and Yagi Y.S. Integrating color and shape-texture features for adaptive real-time object tracking. IEEE Trans. Image Process. 17 2 (2008) 235-240
32. Cui J.S., Zha H.B., Zhao H.J., and Shibasaki R. Multi-model tracking of people using laser scanners and video camera. Image Vis. Comput. 26 2 (2008) 240-252
33. Babu R.V., Perez P., and Bouthemy P. Robust tracking with motion estimation and local kernel based color modeling. Image Vis. Comput. 25 8 (2007) 1205-1216
34. Jeyakar J., Babu R.V., and Ramakrishnan K.R. Robust object tracking with background-weighted local kernels. Comput. Vis. Image Understand. 112 3 (2008) 296-309
35. Leichter I., Lindenbaum M., and Rivlin E. Tracking by affine kernel transformations using color and boundary cues. IEEE Trans. Pattern Anal. Mach. Intell. 31 1 (2009) 164-171
36. Z.L. Jiang, S.F. Li, D.F. Gao, An adaptive mean shift tracking method using multi-scale images, in: International Conference on Wavelet Analysis and Pattern Recognition, 2007, pp. 1060-1066.