Title :
Visual tracking by appearance modeling and sparse representation
Author :
Wang, Qing ; Chen, Feng ; Xu, Wenli
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Abstract :
Appearance variation is a big challenge for object tracking. To deal with this problem, we propose a robust tracking method by online appearance modeling and sparse representation. In this method, we use the intensity matrix of image to represent the object, and learn a low dimensional subspace online to model the object appearance variations during tracking. Then applying the recent theory of sparse representation [1], we construct a likelihood function to measure the similarity between an object candidate and the learned appearance model. After that, tracking is led by the Bayesian inference framework, in which a particle filter is utilized to recursively estimate the object state over time. Theoretic analysis and experiments compared with state-of-the-art methods demonstrate the effectiveness of the proposed algorithm.
Keywords :
Bayes methods; image representation; inference mechanisms; object detection; particle filtering (numerical methods); target tracking; Bayesian inference framework; intensity matrix; likelihood function; low dimensional subspace; object tracking; online appearance modeling; particle filter; robust tracking; sparse representation; visual tracking; Adaptation model; Approximation methods; Computational modeling; Noise; Target tracking; Visualization;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5582847