Target Tracking Based on Feature Space of Detection
AN Guo-chen · 2013
To solve the problem of small targets or targets which have similar color with scene background in tracking,we propose an object tracking method which exploits real-time detection results.First,scene background is effectively modeled and foreground mask is obtained by background subtraction and frame differencing,then,mean shift algorithm is applied to objects tracking in the fused image space.Though precise and sensitive,pixel-level processing algorithms such as mixture of Gaussian and frame differencing are not robust.The mean shift algorithm which is a block-level processing one is robust whereas it weakens spatial information of feature space.This paper effectively combines the merits of both algorithms to achieve robustness and accuracy of object tracking.Through the method,our system shows very good tracking performance for targets which move fast or have similar color disturbance with scene background.Also,our algorithm is efficient and the computation of the novel algorithm is fast to satisfy real-time application.Several groups of comparative experimental results show that the new algorithm can effectively suppress the scene background disturbance and improve the performance of object tracking.Experimental results on video clips demonstrate the effectiveness and efficiency of our method.