Computer Vision, IEEE International Conference on (2005)
Oct. 17, 2005 to Oct. 20, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2005.181
Bohyung Han , University of Maryland at College Park
Larry Davis , University of Maryland at College Park
Object tracking is a challenging problems in real-time computer vision due to variations of lighting condition, pose, scale, and view-point over time. However, it is exceptionally difficult to model appearance with respect to all of those variations in advance; instead, on-line update algorithms are employed to adapt to these changes. We present a new on-line appearance modeling technique which is based on sequential density approximation. This technique provides accurate and compact representations using Gaussian mixtures, in which the number of Gaussians is automatically determined. This procedure is performed in linear time at each time step, which we prove by amortized analysis. Features for each pixel and rectangular region are modeled together by the proposed sequential density approximation algorithm, and the target model is updated in scale robustly. We show the performance of our method by simulations and tracking in natural videos.
B. Han and L. Davis, "On-Line Density-Based Appearance Modeling for Object Tracking," Computer Vision, IEEE International Conference on(ICCV), Beijing, China, 2005, pp. 1492-1499.