Real-time kernel based object tracking using mean shift - Talu, M. Fatih; Türko?lu, Ibrahim; Cebeci, Mehmet (My University, 2010)
In this paper, we improve the real-time object tracking algorithm of Yang [1] which uses a symmetric similarity function between spatially smoothed kernel-density estimates of the model and the target distributions. This spatial smoothed process applied on the centre points of the probability density functions increases not only computational complexity but also noise sensitivity. After reducing background information and using a new simple similarity function between the model and the target distributions, the proposed algorithm successfully coped with camera motion, partial occlusions and clutter with lower computational complexity. For tracking object, the mean shift algorithm is used iteratively. The simplicity of the new similarity function leads to an efficient and robust nonparametric tracking algorithm. The mathematical results obtained about the performance of the proposed algorithm on several image sequences are represented comparatively in a tabular format. ©2010 IEEE.
Ortalama kayma algoritmasi kullanilarak gerçek zamanli çekirdek tabanli nesne takibi
18th IEEE Signal Processing and Communications Applications Conference, SIU 2010 -- 22 April 2010 through 24 April 2010 -- Diyarbakir -- 83388
Keywords: Algorithms; Computational complexity; Image segmentation; Probability density function; Signal processing; Tracking (position); Background information; Camera motions; Density estimates; Image sequence; Mean shift; Mean shift algorithm; Noise sensitivity; Non-parametric; Object Tracking; Partial occlusions; Real-time kernel; Real-time object tracking; Similarity functions; Tracking algorithm; Tracking objects; Target tracking