Title :
Visual object tracking using improved Mean Shift algorithm
Author :
Sulfan Bagus Setyawan;Djoko Purwanto;Ronny Mardiyanto
Author_Institution :
Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Abstract :
Visual object tracking is one of many important applications for surveillance systems. The issues for visual object tracking are robustness from background interference, scaling and occlusion detection. In this paper, visual object tracking using improved Mean Shift algorithm is proposed. Mean Shift algorithm is used to obtain center object target for tracking. Corrected Background Weighted Histogram is added in target model to reduce background interference. Then, Scale adaptive methods is added in Mean Shift for scaling. Occlusion detection is handled by scaled Normalized Cross Correlation. The results prove that the proposed method is robust from noise background, scaling and occlusion detection.
Keywords :
"Mathematical model","Histograms","Object tracking","Target tracking","Robustness","Visualization","Interference"
Conference_Titel :
Information Technology Systems and Innovation (ICITSI), 2015 International Conference on
Print_ISBN :
978-1-4673-6663-2
DOI :
10.1109/ICITSI.2015.7437677