A Robust Median-based Background Updating Algorithm

Abstract

Abstract-Image processing techniques for object tracking, identification and classification have become common today as a result of improved quality of cameras as well as prices of cameras becoming cheaper and cheaper day by day. The use of cameras also make it possible for human analysis of video streams or images where it is difficult for robots or algorithms or machines to effectively deal with the images. However, the use of cameras for basic tracking and analysing do not come without challenges such as issues with sudden changes in illumination, shadows, occlusion, noise, and high computational time and space complexities of algorithms. A typical image processing task may involve several subtasks such as capturing, and pre-processing which demand high computational resources to complete. One of the main pre-processing tasks used in image processing is image segmentation which enables images to be divided into sections of interest in order to perform analysis on them. Background Subtraction is commonly used to segment images into Background and Foreground for further processing. Algorithms producing highly accurate results during this segmentation task normally demand high computation time or memory space, while algorithms that use smaller memory space and shorter time to complete this segmentation task may also suffer from limitations that may lead to undesired results at some point in time. Poor outputs from algorithms will eventually lead to system failure which must be avoided as much as possible. This paper proposes a median based background updating algorithm which determines the median of a buffer containing values that are highly correlated. The algorithm achieves this by deletingan extreme valuefrom the buffer whenever data is to be added to it.Experiments show that the method produces good results with less computational time which will make it possible to implement on devices that do not have much computation resources.

Fast Generation of Image’s Histogram Using Approximation Technique for Image Processing Algorithms

“…approximation technique has been explored in the design of image enhancement techniques such a filtering and image deblurring by using the idea of approximating a pixel's value for better image. A typical filtering algorithm such as the median filtering and all the median based filtering algorithms [26][27] use the statistical median of a pixel's neighbourhood to estimate or determine an ideal value for that particular pixel. This is done because the pixel value may be considered as a noise and these algorithms try to estimate what the actual value should be.…”