Pedestrian Positioning Scheme Based on the Fusion of Smartphone IMU Sensors and Commercially Surveillance Video
Fan Yang, Liuyan Gou, Xingxing Cai · IEEE Sensors Journal · 2022
Existing pedestrian positioning technologies have difficulty balancing multiple aspects such as positioning accuracy, system maintenance, equipment and deployment costs. We propose a pedestrian positioning scheme employing inertial measurement unit (IMU) sensors of smartphones and surveillance video. We apply pedestrian dead reckoning (PDR) information to mark the pedestrians to be located and use surveillance video to track all pedestrians in the field of view. For video pedestrian tracking, we use a modified Markov model to reduce the probability of trajectory merging and interruption. In the fusion of video trajectories, we use single-step feature weighting and multi-step dynamic time warping (DTW) to improve the matching accuracy of trajectories, respectively. The experimental results show that the average positioning error of our proposed fusion scheme is less than 1.5 m.