4-Dimensional deformation part model for pose estimation using Kalman filter constraints. (27th June 2017)

• Record Type: Journal Article
• Authors: Berti, Enrique Martinez; Salmerón, Antonio Jose Sánchez; Viala, Carlos Ricolfe
• Is Part Of: International journal of advanced robotic systems. Volume 14:Number 3(2017:May/Jun.)
• Publication Date: 2017-06-27
• Subjects: DPM -- Kalman filter -- pose estimation -- kinematic constraints -- human activity recognition -- computer vision -- motion and tracking; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881417714230
• Languages: English
• ISSNs: 1729-8806

Abstract

The goal of this research work is to improve the accuracy of human pose estimation using the deformation part model without increasing computational complexity. First, the proposed method seeks to improve pose estimation accuracy by adding the depth channel to deformation part model, which was formerly defined based only on RGB channels, to obtain a 4-dimensional deformation part model. In addition, computational complexity can be controlled by reducing the number of joints by taking into account in a reduced 4-dimensional deformation part model. Finally, complete solutions are obtained by solving the omitted joints by using inverse kinematic models. The main goal of this article is to analyze the effect on pose estimation accuracy when using a Kalman filter added to 4-dimensional deformation part model partial solutions. The experiments run with two data sets showing that this method improves pose estimation accuracy compared with state-of-the-art methods and that a Kalman filter helps to increase this accuracy.