Improved Background Subtraction Technique for Detecting Moving Objects

背景减法 计算机科学 人工智能 计算机视觉 水准点(测量) 目标检测 对象(语法) 噪音(视频) 滤波器(信号处理) 提取器 领域(数学) 中值滤波器 图像(数学) 模式识别(心理学) 图像处理 像素 数学 工程类 大地测量学 工艺工程 纯数学 地理
作者
Tannistha Pal
出处
期刊:Recent advances in computer science and communications [Bentham Science]
卷期号:14 (9): 2854-2862 被引量:2
标识
DOI:10.2174/2666255813999200817172733
摘要

Introduction: Moving object detection from videos is among the most difficult task in different areas of computer vision applications. Among the traditional object detection methods, researchers conclude that Background Subtraction method carried out better in aspects of execution time and output quality. Mehtod: Visual background extractor is a renowned algorithm in Background Subtraction method for detecting moving object in various applications. In the recent years, lots of work has been carried out to improve the existing Visual Background extractor algorithm. Result: After investigating many state of art techniques and finding out the research gaps, this paper presents an improved background subtraction technique based on morphological operation and 2D median filter for detecting moving object which reduces the noise in the output video and also enhances its accuracy at a very limited additional cost. Experimental results in several benchmark datasets confirmed the superiority of the proposed method over the state-of-the-art object detection methods. Conclusion: In this article, a method has been proposed for moving object detection where the quality of the output object is enhanced and good accuracy is achieved. This method provide with accurate experimental results, which helps in efficient object detection. The proposed technique also deals with Visual Background extractor Algorithm along with the Image Enhancement Procedure like Morphological and 2-D Filtering at a limited additional cost Discussion: This article worked on certain specific field, like noise reduction and image enhancement of output images of the existing ViBe Algorithm. The technique proposed in this article will be beneficial for various computer vision applications like video surveillance, road condition monitoring, airport safety, human activity analysis, monitoring marine border for security purpose etc.

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