Abstract
The goal of this research work is to improve the accuracy of human pose estimation using the Deformation Part Model (DPM) without increasing computational complexity. First, the proposed method seeks to improve pose estimation accuracy by adding the depth channel to DPM, which was formerly defined based only on red–green–blue (RGB) channels, in order to obtain a four-dimensional DPM (4D-DPM). In addition, computational complexity can be controlled by reducing the number of joints by taking it into account in a reduced 4D-DPM. Finally, complete solutions are obtained by solving the omitted joints by using inverse kinematics models. In this context, the main goal of this paper is to analyze the effect on pose estimation timing cost when using dual quaternions to solve the inverse kinematics.
Highlights
Human pose estimation has been extensively studied for many years in computer vision.Many attempts have been made to improve human pose estimation with methods that work mainly with monocular red–green–blue (RGB) images such as [1,2,3,4,5].With the ubiquity and increased use of depth sensors, methods that use red–green–blue-depthRGB-D imagery are fundamental
Another drawback of the algorithm is the large number of training examples that are required to train its deep random forest algorithm, and which could make training cumbersome. Another drawback of [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. To alleviate these and other drawbacks in [6], we propose a novel approach that takes advantage of both RGB and depth information combined in a multi-channel mixture of parts for pose estimation in single frame images coupled with a skeleton constrained linear quadratic estimator (Kalman filter) that uses the rigid information of a human skeleton to improve joint tracking in consecutive frames
The main contribution of our method extends to: (i) a multi-channel mixture of parts model that allows the detection of parts in RGBD images; (ii) a linear quadratic estimator (KF) that employs rigid information and connected joints of human pose; (iii) a model for unsolved joints through inverse kinematics that allows the model to be trained with fewer joints and in less time
Summary
Human pose estimation has been extensively studied for many years in computer vision.Many attempts have been made to improve human pose estimation with methods that work mainly with monocular red–green–blue (RGB) images such as [1,2,3,4,5].With the ubiquity and increased use of depth sensors, methods that use red–green–blue-depthRGB-D imagery are fundamental. Human pose estimation has been extensively studied for many years in computer vision. Many attempts have been made to improve human pose estimation with methods that work mainly with monocular red–green–blue (RGB) images such as [1,2,3,4,5]. One of the methods that used such imagery, and which is currently considered the state of the art for human pose estimation, is Shotton et al [6], which was commercially developed for the kinect device (Microsoft, Redmond, WA, USA). Shotton’s method allows real-time joint detection for human pose estimation based solely on depth channel. Despite the state-of-the-art performance of [6] and the commercial success of kinect, the many drawbacks of [6] make it difficult to be adopted in any other type of three-dimensional (3D) computer vision system
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