Mean Shift object tracking algorithm assisted by depth cues
Song Kangkan · Computer Engineering and Applications Journal · 2013
The background noise in the candidate object model diminishes the object color characteristic, and induces localization error. To reduce the error, according to the discriminative depth level between the object's and the background's, a Mean Shift algorithm based on depth cues assisted and corrected background-weighted histogram is proposed. The proposed algorithm can sufficiently weaken the background noisy interference in the kernel window, enhance the object's color feature information, and update the kernel size adaptively in due course to reduce the distractive information in the background as the object size becomes small. Experimental result shows the proposed algorithm has fewer iteration number and good localization precision of tracking.