Using Interval Particle Filtering for Marker less 3D Human Motion Capture

TL;DR

This paper introduces a novel markerless 3D human motion capture method using interval particle filtering, enabling effective tracking from single camera feeds without restrictive models, suitable for gait analysis.

Contribution

The paper presents a new interval particle filtering algorithm that efficiently searches the configuration space for markerless 3D human motion capture from monocular video.

Findings

Successfully tracks human motion with single camera

Performs well even with partial occlusions

Comparable accuracy to marker-based systems

Abstract

In this paper we present a new approach for marker less human motion capture from conventional camera feeds. The aim of our study is to recover 3D positions of key points of the body that can serve for gait analysis. Our approach is based on foreground segmentation, an articulated body model and particle filters. In order to be generic and simple no restrictive dynamic modelling was used. A new modified particle filtering algorithm was introduced. It is used efficiently to search the model configuration space. This new algorithm which we call Interval Particle Filtering reorganizes the configurations search space in an optimal deterministic way and proved to be efficient in tracking natural human movement. Results for human motion capture from a single camera are presented and compared to results obtained from a marker based system. The system proved to be able to track motion successfully even in partial occlusions.