Real-Time Infrared Object Tracking Based on Mean Shift

Jian Cheng, Jie Yang

Abstract: Themean shift algorithm is an efficient method for tracking object in the color image sequence. However, in the infrared object-tracking scenario, there is a singular feature space, i.e. the grey space, for representing the infrared object. Due to the lack of the information for the object representation, the object tracking based on the mean shift algorithm may be lost in the infrared sequence. To overcome this disadvantage, we propose a new scheme that is to construct a cascade grey space. The experimental results performed on two different infrared image sequences show our new scheme is efficient and robust for the infrared object tracking.