JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Rotation Invariant Tracking-Learning-Detection System
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Rotation Invariant Tracking-Learning-Detection System
Choi, Wonju; Sohn, Kwanghoon;
  PDF(new window)
 Abstract
In recent years, Tracking-Learning-Detection(TLD) system has been widely used as a detection and tracking algorithm for vision sensors. While conventional algorithms are vulnerable to occlusion, and changes in illumination and appearances, TLD system is capable of robust tracking by conducting tracking, detection, and learning in real time. However, the detection and tracking algorithms of TLD system utilize rotation-variant features, and the margin of tracking error becomes greater when an object makes a full out-of-plane rotation. Thus, we propose a rotation-invariant TLD system(RI-TLD). we propose a simplified average orientation histogram and rotation matrix for a rotation inference algorithm. Experimental results with various tracking tests demonstrate the robustness and efficiency of the proposed system.
 Keywords
Tracking-Learning-Detection;TLD;Rotation Invariant;Simplified Average Orientation Histogram;
 Language
Korean
 Cited by
 References
1.
I. Haritaoglu, D. Harwood, and L.S. Davis, "W4: Real-Time Surveillance of People and Their Activities," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 809-830, 2000. crossref(new window)

2.
F. Dellaert and R. Collins, "Fast Image-Based Tracking by Selective Pixel Integration," Proceeding of International IEEE Conference on Computer Vision Workshop on Frame-Rate Vision, pp. 1-22, 1999.

3.
S. Zhou, R. Chellappa, and B. Moghaddam, “Visual Tracking and Recognition Using Appearance-Adaptive Models in Particle Filters,” IEEE Transactions on Image Processing, Vol. 13, No. 11, pp. 1491-1506, 2004. crossref(new window)

4.
K. Okuma, A. Taleghani, and N.D. Freitas, "A Boosted Particle Filter : Multitarget Detection and Tracking," Proceeding of European Conference on Computer Vision, pp. 28-39, 2004.

5.
J. Kim, C. Park, and I. Kweon, "Visual Tracking for Non-rigid Objects Using Rao-Blackwell zed Particle Filter," Proceeding of IEEE International Conference on Robotices and Automation, pp. 4537-4544, 2010.

6.
H. Grabner and H. Bischof, "On-line Boosting and Vision," Proceeding of IEEE Computer Vision and Pattern Recognition, Vol. 1, pp. 260-267, 2006.

7.
D. Lee, “Effective Gaussian Mixture Learning for Video Background Subtraction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 27, No. 5, pp. 827-832, 2005. crossref(new window)

8.
D.A. Ross, J. Lim, R. Lin, and M. Yang, “Incremental Learning for Robust Visual Tracking,” Transactions on International Journal of Computer Vision, Vol 77, No. 1, pp. 125-141, 2008. crossref(new window)

9.
D. Comaniciu and P. Meer, “Mean Shift : A Robust Approach Toward Feature Space Analsis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 24, No. 5, pp. 603-619, 2002. crossref(new window)

10.
A. Yilmaz, "Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection," Proceeding on IEEE Computer Vision and Pattern Recognition, pp. 1-6, 2007.

11.
Z. Kalal, K. Mikolajczyk, and J. Matas, "Tracking-learning-detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, No. 7, pp. 1409-1422, 2012. crossref(new window)

12.
G. Nebehay, Robust Object Tracking Based on Tracking-learning-detection, Master's Thesis of Technische Universität Wien, 2012.

13.
H. Shi, Z. Lin, W. Tang, B. Liao, and J. Wang, "A Robust Hand Tracking Approach Based on Modified Tracking-learning-detection Algorithm,” Journal of Multimedia and Ubiquitous Engineering, Vol. 308, No. 1, pp. 9-15, 2014. crossref(new window)

14.
P. Guo, X. Li, S. Ding, Z. Tian, and X. Zhang, "Adaptive and Accelerated Tracking-learning-detection," Proceeding of International Symposium on Photoelectronic Detection and Imaging, pp. 89082H-89082H, 2013.

15.
W. Hailong, W. Guangyu, and L. Jianxun, "An Improved Tracking-learning-detection Method," Proceeding of IEEE Chinese Control Conference, pp. 3858-3863, 2015.

16.
D.G. Lowe, "Distinctive Image Features from Scale-invariant Keypoints," Transactions on International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2014. crossref(new window)

17.
M. Calonder, V. Lepetit, C. Strecha, and P. Fua, "Brief: Binary Robust Independent Elementary Features," European Conference on Computer Vision, pp. 778-792, 2010.

18.
WHOANG, In-Teck; CHOI, Kwang-Nam, "An Algorithm for Color Object Tracking," Journal of Korea Multimedia Society, Vol. 10, No. 7, pp. 827-837, 2007.