Object detection and tracking using SIFT-KNN classifier and Yaw-Pitch servo motor control on humanoid robot

Abstract

Computer vision is a technology intended to replace the visual function in humans with extracting information and features from an image and analyzing the information. In this paper discussed the process of design and implementation of object tracking system that was built starting with object recognition in the early stages and then equipped with yaw and pitch system for tracking the position of the object on bioloid GP. SIFT algorithm is used as a feature extraction, KNN is used as a classifier and for estimation of homographic changes in objects using RANSAC. In order for objects to be tracked automatically, PID control is used to correct the coordinates obtained when object recognition with center coordinates of the frame. So, the system can track the object with single or dynamic displacement.