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.
Mobile Web and Intelligent Information Systems - 14th MobiWIS 2017, Prague, Proceedings _ Muhammad Younas _ Springer