Notice of Removal: Vehicle detection based on self-adaptive background updating
Hao Xiaoli, Yang Maoqing, Xing Yang · 2012
In order to cover the shortages of frame difference and background subtraction in vehicle detection, a method based on self-adaptive background updating is proposed. Self-adaptive background updating, the key part of the proposed, is to update the background template only in the case that virtual loop is empty. Experimental results have indicated that the proposed is simple and efficient in vehicle detection under different lighting conditions; and that its average executive time for each vehicle and success rate is 15ms and 97.2% respectively.