CVABS: Moving Object Segmentation with Common Vector Approach for Videos

TL;DR

This paper introduces a novel subspace-based background modeling algorithm using the Common Vector Approach for real-time moving object segmentation in videos, demonstrating effectiveness across various dynamic background scenarios.

Contribution

The paper presents a new background modeling method employing the Common Vector Approach with Gram-Schmidt orthogonalization, enhancing moving object detection in dynamic environments.

Findings

Effective on CDNet2014 dataset

Handles dynamic backgrounds successfully

Utilizes self-learning feedback for background updating

Abstract

Background modelling is a fundamental step for several real-time computer vision applications that requires security systems and monitoring. An accurate background model helps detecting activity of moving objects in the video. In this work, we have developed a new subspace based background modelling algorithm using the concept of Common Vector Approach with Gram-Schmidt orthogonalization. Once the background model that involves the common characteristic of different views corresponding to the same scene is acquired, a smart foreground detection and background updating procedure is applied based on dynamic control parameters. A variety of experiments is conducted on different problem types related to dynamic backgrounds. Several types of metrics are utilized as objective measures and the obtained visual results are judged subjectively. It was observed that the proposed method stands successfully for all problem types reported on CDNet2014 dataset by updating the background frames with a self-learning feedback mechanism.