An Efficient HumanFollowing Method by Fusing Kernelized Correlation Filter and Depth Information for Mobile Robot Humanfollowing ability enables mobile robots to cooperate with human beings smartly. The kernelized correlation filter (KCF) was typically adopted as a human tracker during the humanfollowing process due to its advantages of fast speed and high precision. However, it is easy to drift away owing to the changes of human pose. In this study, we proposed an efficient humanfollowing method by fusion of KCF and depth information for the mobile robot. The target human was separated from the background regions based on the seeded region growing. KCF tracker outputs and segmentation results were fused by using the adaptive weighted fusion method. The weighting factors were adjusted based on the tracking quality indicators, which reflect the tracking reliability of KCF and the distribution of depth information. Experimental results verify the effectiveness of the proposed method in terms of tracking speed and stability. The center location error of all frames was less than 48 pixels and the bounding box overlap rate of all frames was larger than 0.62.

Multiple Object Tracking for Dense Pedestrians by Markov Random Field Model with Improvement on Potentials Pedestrian tracking in dense crowds is a challenging task, even when using a multicamera system. In this paper, a new Markov random field (MRF) model is proposed for the association of tracklet couplings. Equipped with a new potential function improvement method, this model can associate the small tracklet coupling segments caused by dense pedestrian crowds. The tracklet couplings in this paper are obtained through a data fusion method based on image mutual information. This method calculates the spatial relationships of tracklet pairs by integrating position and motion information, and adopts the human key point detection method for correction of the position data of incomplete and deviated detections in dense crowds. The MRF potential function improvement method for dense pedestrian scenes includes assimilation and extension processing, as well as a message selective belief propagation algorithm. The former enhances the information of the fragmented tracklets by means of a soft link with longer tracklets and expands through sharing to improve the potentials of the adjacent nodes, whereas the latter uses a message selection rule to prevent unreliable messages of fragmented tracklet couplings from being spread throughout the MRF network. With the help of the iterative belief propagation algorithm, the potentials of the model are improved to achieve valid association of the tracklet coupling fragments, such that dense pedestrians can be tracked more robustly. Modular experiments and systemlevel experiments are conducted using the PETS2009 experimental data set, where the experimental results reveal that the proposed method has superior tracking performance.