Fast detecting and tracking algorithm of Infrared target under complex background
Guowang Gao, Lipin Li, Jiuxu Song, Hanlin Qin · 2011
A tracking algorithm of Infrared target is proposed that is the combination of non-linear edge detection and Mean Shift method. The non-linear edge detection algorithm employs dual-window arithmetic operators that has the advantage of few calculation amount, high speed, good image quality and so on. The result of edge detection is binary images. Based on these information, the Mean Shift method is improved to implement target tracking. The tracking algorithm of improved Mean Shift combines the information of the local standard deviation calculation of the target area, describes the target based on the probability density function about gray value and the local standard deviation and selects cascade kernel function to calculate the target density that make up the shortage only using gray to describe the target features. Experimental results show that the edge of infrared target under complex background is detected clearly and infrared target is auto-tracked accurately.