Security Guard Robot Detecting Human Using Gaussian Distribution Histogram Method

Abstract

Problem statement: The purpose of any robotic is to perform tasks that a human would prefer not to do or hopefully do it with precision in order to avoid mistakes or when a human is out of duty due to fatigue or health reasons. The research into human detection into images has paid the way be aware of what is going on around the houses or buildings where a front-line security is needed 24 h a day. In this research a human detection security robot based on Gaussian distribution histogram was proposed. Approach: The proposed method consisted of three steps: (1) the RGB color space histogram was created by subdividing a color space into certain number of bins and then counted the number of pixels that each bin contains. (2) The created RGB histogram was converted into HSV color histogram using Gaussian distribution method. (3) The bell-shape curve of the Gaussian distribution was used to calculate the detection probability between the standard deviation. Results: Experimental evaluation had been tested on the images sequences where the experimental results revealed that the proposed method was less sensitive to changes in the scene achieving higher performance detection than traditional method of histogram creation and had been found to be robust. Conclusion: The results showed that the histogram based human detection resists to any changes in the image scenes.