Convolutional neural network based tracking for human following mobile robot with LQG based control system

Abstract

Visual object Tracking is one of the most challenging tasks in computer vision due to various complications like environmental clutter and object clutter. In this paper we propose the use of Masked RCNN and YoloV2 based CNN architecture to overcome the challenges of tracking and we have also compared their performance in real-time application on a Mobile Robot. The type of dataset required, and approach considered for each of the approach to increase the accuracy as well as implantability on real-time system is also discussed. A Skid Steer Mobile Robot (SSMR) is used to follow the human detected by the CNN algorithms. Te Robot Control is done by use of Linear Quadratic Gaussian Controller for velocity control.