This paper is published in Volume-4, Issue-1, 2018
Area
Image Processing
Author
Remya K. V, Vipin Krishnan C. V
Org/Univ
Cochin College of Engineering and Technology, Valanchery, Kerala, India
Keywords
Visual Object Tracking,Modeling Techniques , Online Matric Learning.
Citations
IEEE
Remya K. V, Vipin Krishnan C. V. Survey of Generative and Discriminative Appearance Models in Visual Object Tracking, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Remya K. V, Vipin Krishnan C. V (2018). Survey of Generative and Discriminative Appearance Models in Visual Object Tracking. International Journal of Advance Research, Ideas and Innovations in Technology, 4(1) www.IJARIIT.com.
MLA
Remya K. V, Vipin Krishnan C. V. "Survey of Generative and Discriminative Appearance Models in Visual Object Tracking." International Journal of Advance Research, Ideas and Innovations in Technology 4.1 (2018). www.IJARIIT.com.
Remya K. V, Vipin Krishnan C. V. Survey of Generative and Discriminative Appearance Models in Visual Object Tracking, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Remya K. V, Vipin Krishnan C. V (2018). Survey of Generative and Discriminative Appearance Models in Visual Object Tracking. International Journal of Advance Research, Ideas and Innovations in Technology, 4(1) www.IJARIIT.com.
MLA
Remya K. V, Vipin Krishnan C. V. "Survey of Generative and Discriminative Appearance Models in Visual Object Tracking." International Journal of Advance Research, Ideas and Innovations in Technology 4.1 (2018). www.IJARIIT.com.
Abstract
Visual object tracking is a challenging task in computer vision applications. The basic statistical appearance modeling techniques are discriminative and generative. In both cases, online learning is very essential to nullify the error due to large pose changes, illumination variations and appearance changes of the tracking framework. This paper briefly introduces the challenges and applications of visual tracking and focuses on discussing the state-of-the-art online-learning based tracking methods by category. In this paper, the existing statistical schemes for tracking-by-detection are reviewed according to their appearance model creation mechanism; generative and discriminative.
