Background subtraction for static & moving camera

Background subtraction is one of the most commonly used components in machine vision systems. Despite the numerous algorithms proposed in the literature and used in practical applications, key challenges remain in designing a single system that can handle diverse environmental conditions. In this paper we present Multiple Background Model based Background Subtraction Algorithm as such a candidate. The algorithm was originally designed for handling sudden illumination changes. The new version has been refined with changes at different steps of the process, specifically in terms of selecting optimal color space, clustering of training images for Background Model Bank and parameter for each channel of color space. This has allowed the algorithm's applicability to wide variety of challenges associated with change detection including camera jitter, dynamic background, Intermittent Object Motion, shadows, bad weather, thermal, night videos etc. Comprehensive evaluation demonstrates the superiority of algorithm against state of the art.

1. Introduction

Background Subtraction (BS) is one of pre-processing steps in many computer vision applications such as surveillance, tracking, privacy etc. A typical BS process involves building a background model of the scene and then for an input image produce a binary mask to separate the foreground (FG) from the background (BG).