Mobile robot can detect and track moving object through its vision sys tem.In order to reduce both false positive rate (FPR) and false negative rate (FNR) of detection,an improved Gaussian mixture model (GMM) for dynamic target real-time detection is presented in this pape r.In this improved algorithm,the concept of block is introduced,then the number and learning rate of Gaussian mixture model can be adjusted dynamically during the updating proces s,and finally the matching criterion of GMM is improved to decrease the false positive rate.After detection,a fusion tr acking algorithm based on mean shift and particle filter (PF) is adopted to promote real-time capability of tr acking system,the particles are scattered around the optimal candidate region obtained by mean shift algorithm, and the quantity of particles is adaptively changed according to mean shift value.This fusion algor ithm combines the advantages of the mean shift and particle filter,has a fast converg ence speed and is robust to occlusion.Experiments implemented on video frames show that the two propose d improved methods not only give higher accuracy than traditional algorithms,but also can detect a nd track the moving object efficiently in mobile robots′ vision system.