Pedestrians tracking based on least squares algorithm and intelligent collision avoidance model
Weicheng Sun, Songhao Zhu, Baoxiao Fu, Yanyun Cheng, Fang Fang · 2017
Target tracking is a hot topic in the field of computer vision and pattern recognition. The aim of target tracking is to achieve the location of moving targets and tracking trajectories of moving targets. A pedestrian tracking method based on the Least Squares algorithm and intelligent collision avoidance model is proposed in this paper. Specifically, the traditional Kalman algorithm is first utilized to realize the initial target tracking; then, to deal with the issue of target tracking caused by the traditional Kalman algorithm, the least square method is here utilized to fit the pedestrian moving curve and predict the location of the pedestrians in the next frame, which can be utilized as the initial moving object for the later search; finally, the intelligent collision avoidance algorithm is here proposed to improve the tracking accuracy in case of obstacles.