Robust Statistical Approach for Extraction of Moving Human Silhouettes from Videos

TL;DR

This paper introduces a robust method combining Gaussian Mixture Models and HSV color space for extracting human silhouettes from videos, improving accuracy in challenging conditions for applications like pose estimation and activity recognition.

Contribution

It proposes a novel approach that integrates GMM with HSV color space for more reliable silhouette extraction under varying conditions.

Findings

Effective background subtraction in diverse environments

Improved silhouette quality for pose estimation

Robustness to illumination and appearance variations

Abstract

Human pose estimation is one of the key problems in computer vision that has been studied in the recent years. The significance of human pose estimation is in the higher level tasks of understanding human actions applications such as recognition of anomalous actions present in videos and many other related applications. The human poses can be estimated by extracting silhouettes of humans as silhouettes are robust to variations and it gives the shape information of the human body. Some common challenges include illumination changes, variation in environments, and variation in human appearances. Thus there is a need for a robust method for human pose estimation. This paper presents a study and analysis of approaches existing for silhouette extraction and proposes a robust technique for extracting human silhouettes in video sequences. Gaussian Mixture Model (GMM) A statistical approach is combined with HSV (Hue, Saturation and Value) color space model for a robust background model that is used for background subtraction to produce foreground blobs, called human silhouettes. Morphological operations are then performed on foreground blobs from background subtraction. The silhouettes obtained from this work can be used in further tasks associated with human action interpretation and activity processes like human action classification, human pose estimation and action recognition or action interpretation.