Open AccessExtended social force model‐based mean shift for pedestrian tracking under obstacle avoidance
Author(s) -
Zhang Xuguang,
Zhang Xufeng,
Wang Yiming,
Yu Hui
Publication year - 2017
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2016.0022
Subject(s) - social force model , obstacle , position (finance) , pedestrian , tracking (education) , frame (networking) , computer science , computer vision , trajectory , artificial intelligence , mean shift , task (project management) , obstacle avoidance , simulation , control theory (sociology) , engineering , mobile robot , pattern recognition (psychology) , robot , law , physics , pedagogy , systems engineering , psychology , telecommunications , political science , finance , astronomy , transport engineering , economics , control (management)
It has been shown that mean shift tracking algorithm can achieve excellent results in pedestrian tracking task. It empirically estimates the target position of current frame by locating the maximum of a density function from the local neighborhood of the target position of previous frame. However, this method only considers its past trajectory without considering the influence of pedestrian environment when applying to pedestrian tracking. In practical, pedestrians always keep a safe distance away from obstacles when programming their paths. To address the issue of obstacle avoidance, this paper proposes a novel extended social force model‐based mean shift tracking algorithm in which pedestrian environment is full taken in consideration. Firstly, an extended social force model is presented to quantify the interaction between pedestrian and obstacle by means of force. Furthermore, directional weights and speed weights are introduced to adjust the strength of the force in terms of the difference of individual perspectives and relative velocities. Finally, the initial target position is predicted by Newton's laws of motion and then the Mean Shift method is integrated to track target position. Experiment results show that this algorithm achieves an encouraging performance when obstacles exist.