Solar Tracking Using Extended Mean Shift Based Color Histogram

Abstract

Nowadays, there are many solar tracking applications using photodiode sensors and Solar Position Algorithm.This tracking depends on the power of light and natural conditions.Inaccurate sun tracking causes the heat concentration to become weak and miss focus on heat-receiving objects.We developed a tracking algorithm to track the sun to support the control system of the dual parabolic concentrator.This algorithm is based on Extended Mean shift to find the tracking position of an object in a video sequence.This algorithm is effective since it exploits the estimation of kernel density for searching the local maximum of a similarity measurement of the color histogram.The Expectation Maximization algorithm functions to estimate model parameters and update the histogram display.An updated color histogram will improve the average shift tracking accuracy and reliability.We successfully applied this algorithm for solar tracking using 148 frames of data.In this experiment, the results obtained in the form of the average value of the color similarity of an object tracking with a truth tolerance percentage of 98.39%.