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 ...
Highlights
Human pose estimation has been extensively studied for many years in computer vision
To alleviate these and other drawbacks in Shotton et al.,[6] we propose a novel approach that takes advantage of both RGB and depth information combined in a multichannel mixture of parts for pose estimation in single frame images coupled with a skeleton constrained linear quadratic estimator Kalman filter (SLQE KF) that uses the rigid information of a human skeleton to improve joint tracking in consecutive frames
In order to track the human skeleton, we model it as a group of kinematic chains, where each part and joint in the human body corresponds to a link and joint in a kinematic chain
Summary
Human pose estimation has been extensively studied for many years in computer vision. Another drawback of Shotton et al.’s algorithm[6] is that its model is trained only on depth information and discards potentially important information that could be found in the RGB channels and could help approach human poses more accurately. The main contribution of our method extends to (i) an optimized multichannel mixture of parts model that allows the detection of parts in RGBD images; (ii) a linear quadratic estimator (LQE KF) that employs rigid information and connected joints of human pose; (iii) after adding depth information, time complexity was adversely affected. Our results show significant improvements over the state-of-the-art in both the publicly available CAD60 data set and our own data set
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
