This paper presents an algorithm of robot visual moving-object tracking (MOT) based on the probabilistic object model with a pe-destrian detector. Three major research topics investigated in the study include the combination of moving feature detection and pe-destrian detection, the improvement of probabilistic object model, and the tuning mechanism of object model training. The developed MOT was further integrated with the visual simultaneous localiza-tion and mapping (vSLAM) to form a simultaneous localization, mapping, and moving object tracking system. The extended Kalman filter (EKF) was used to estimate the system states and the speeded-up robust features (SURFs) were employed to represent the visual environment map. Experiments were carried out in this research to validate the performance of the developed systems.
Relation:
Mobile Web and Intelligent Information Systems - 14th MobiWIS 2017, Prague, Proceedings _ Muhammad Younas _ Springer
