Object Tracking Based on the Weighted Lucas-Kanade Algorithm
Rongsheng Zeng · Guangdian gongcheng · 2011
The traditional object tracking methods are difficult to deal with the interference of complex background,the variety of object shape and the irregular change of object position.A new object tracking method based on the weighted Lucas-Kanade algorithm is proposed.Firstly,a reasonable estimation of object position in current frame is obtained according to the search template.This position is used as initial iteration parameter of the weighted Lucas-Kanade algorithm.Secondly,the weights function is calculated and the accurate object position is achieved by tracking the current template and the initial template in current frame.Finally,the template update strategies under the complex background and the object shape variety are implemented.Experimental results based on a large amount of measured data show that the proposed method can effectively realize stable tracking of object in complex background.