Reliable people tracking approach for mobile robot in indoor environments

There is a great challenge that a mobile robot reliably and continuously tracks a specific person in indoor environments. In this paper, a novel method is presented, which can effectively recognize and reliably track a target person based on mobile robot vision. Gabor wavelet and hidden Markov model (HMM) are firstly employed for identifying the target person. In order to effectively track the specific person and reduce the computational cost in tracking stage, horizontal-projecting probability histogram (HPPH) of upper body color clothes region is proposed for extracting the pattern features, which significantly improves the tracking reliability and, at the same time, unscented particle filter (UPF) is integrated and PID operator is introduced for controlling the robot to follow the person. Experimental results validate the robustness and the reliability of this approach.