Background Subtraction using Compressed Low-resolution Images

TL;DR

This paper introduces a novel background subtraction method that utilizes compressed, low-resolution grayscale images, significantly improving efficiency while maintaining salient information for real-time applications.

Contribution

The paper presents a new background subtraction approach using compressed low-resolution images, enhancing real-time processing in visual tasks.

Findings

Effective background subtraction with low-resolution images

ViBe and GMM methods confirmed the approach's effectiveness

Preserves salient information in compressed images

Abstract

Image processing and recognition are an important part of the modern society, with applications in fields such as advanced artificial intelligence, smart assistants, and security surveillance. The essential first step involved in almost all the visual tasks is background subtraction with a static camera. Ensuring that this critical step is performed in the most efficient manner would therefore improve all aspects related to objects recognition and tracking, behavior comprehension, etc.. Although background subtraction method has been applied for many years, its application suffers from real-time requirement. In this letter, we present a novel approach in implementing the background subtraction. The proposed method uses compressed, low-resolution grayscale image for the background subtraction. These low-resolution grayscale images were found to preserve the salient information very well. To verify the feasibility of our methodology, two prevalent methods, ViBe and GMM, are used in the experiment. The results of the proposed methodology confirm the effectiveness of our approach.