Development of a Highly Accurate Person Tracking System for Home Service Robots

Abstract

In this paper, we propose a highly accurate specific person tracking system for home service robots. This study focuses on Selected Online Ada-Boosting (SOAB) and Kernelized Correlation Filters (KCF). It proposes the Selected KCF (SKCF), which combines features of fast adaptation to a specific person and robustness for occlusion. We also propose a system that combines You Only Look Once (YOLO), a real-time object detection algorithm, with SKCF to initialize and recover the tracking system. The proposed system runs at 17 [fps] in an experimental environment with an Intel Core i7-8700K CPU and an NVIDIA GTX 1080 GPU. We also show that the tracking accuracy of the proposed system on public datasets for tracking specific persons is better than that of conventional methods.