Adaptive visual tracking based on discriminative feature selection for mobile robot

The main challenges of visual tracking for mobile robot come from variation of target´s appearance and disturbance of environment, such as pose changes of target, illumination changes, and cluttered background. This paper presents a robust adaptive visual tracker which is able to capture the varying appearance of target under different environments without gradual drift. We propose a novel and flexible feature space evaluation function which is formed by the weighted sum of two components: the similarity measure and the discriminating ability measure. To minimize the influence of background, a new salient feature selection mechanism is proposed to clearly distinguish between target and background. A novel target model updating mechanism is introduced to avoid gradual model drift with time, and a pure, adaptive and time-continuous target model is obtained for each input frame without off-line training and prior knowledge. The proposed discriminative feature selection and target model updating mechanism is embedded in a Mean-shift tracking system which iteratively finds the nearest local optimal localization of target. Experimental results on a mobile robot system demonstrate the robust performance of the proposed algorithm under different challenging conditions.