scholarly journals Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 122 ◽  
Author(s):  
Chi-Yi Tsai ◽  
Kuang-Jui Hsu ◽  
Humaira Nisar
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Image Features ◽  
Estimation Methods ◽  
Reference Image ◽  
Practical Applications ◽  
Pose Tracking ◽  
Model Based ◽  
Object Pose Estimation ◽  
Template Tracking

Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality.

scholarly journals Real-Time Energy Efficient Hand Pose Estimation: A Case Study

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2828
Author(s):  
Mhd Rashed Al Koutayni ◽  
Vladimir Rybalkin ◽  
Jameel Malik ◽  
Ahmed Elhayek ◽  
Christian Weis ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Pose Estimation ◽  
Energy Efficient ◽  
Graphics Processing Units ◽  
Estimation Algorithm ◽  
High Energy ◽  
Estimation Methods ◽  
Hand Pose Estimation ◽  
Hand Pose

The estimation of human hand pose has become the basis for many vital applications where the user depends mainly on the hand pose as a system input. Virtual reality (VR) headset, shadow dexterous hand and in-air signature verification are a few examples of applications that require to track the hand movements in real-time. The state-of-the-art 3D hand pose estimation methods are based on the Convolutional Neural Network (CNN). These methods are implemented on Graphics Processing Units (GPUs) mainly due to their extensive computational requirements. However, GPUs are not suitable for the practical application scenarios, where the low power consumption is crucial. Furthermore, the difficulty of embedding a bulky GPU into a small device prevents the portability of such applications on mobile devices. The goal of this work is to provide an energy efficient solution for an existing depth camera based hand pose estimation algorithm. First, we compress the deep neural network model by applying the dynamic quantization techniques on different layers to achieve maximum compression without compromising accuracy. Afterwards, we design a custom hardware architecture. For our device we selected the FPGA as a target platform because FPGAs provide high energy efficiency and can be integrated in portable devices. Our solution implemented on Xilinx UltraScale+ MPSoC FPGA is 4.2× faster and 577.3× more energy efficient than the original implementation of the hand pose estimation algorithm on NVIDIA GeForce GTX 1070.

scholarly journals A Survey on Model-Based Fault Detection Techniques for Linear Time-Invariant Systems with Numerical Analysis

2021 ◽  
Vol 30 (1) ◽  
pp. 53-78
Author(s):  
Masood Ahmad ◽  
Rosmiwati Mohd-Mokhta
Keyword(s):  
Fault Detection ◽  
Linear Time ◽  
Fault Detection And Isolation ◽  
Estimation Methods ◽  
Future Research ◽  
Practical Applications ◽  
Detection Techniques ◽  
Linear Time Invariant ◽  
Model Based ◽  
Time Invariant

With the ongoing increase in complexity, less tolerance to performance degradation and safety requirements of practical systems has increased the necessity of fault detection (FD) as early as possible. During the last few decades, many research findings have been developed in fault diagnosis that addresses the issue of fault detection and isolation in linear and nonlinear systems. The paper’s objective is to present a survey on various state-of-art model-based FD techniques developed for linear time-invariant (LTI) systems for the interested readers to learn about recent development in this field. Model-based FD techniques for LTI systems are classified as parameter-estimation methods, parity-space-based methods, and observer-based methods. The background and recent progress, in context to fault detection, of each of these methods and their practical applications are discussed in this paper. Furthermore, two different FD techniques are compared via analytical equations and simulation results obtained from the DC motor model. In the end, possible future research directions in model-based FD, particularly for the LTI system, are highlighted for prosperous researchers. A comparison and emerging research topic make this contribution different from the existing survey papers on FD.

scholarly journals Real-time pose tracking of 3D targets by photo-model-based stereo-vision

2020 ◽  
Vol 14 (4) ◽  
pp. JAMDSM0057-JAMDSM0057
Author(s):  
Hongzhi TIAN ◽  
Yejun KOU ◽  
Xiang LI ◽  
Mamoru MINAMI
Keyword(s):  
Real Time ◽  
Stereo Vision ◽  
Pose Tracking ◽  
Model Based

Model-Based 3D Object Pose Estimation Using Occluding Contours

2011 ◽  
Vol 131 (4) ◽  
pp. 505-514
Author(s):  
Kenichi Maruyama ◽  
Yoshihiro Kawai ◽  
Fumiaki Tomita
Keyword(s):  
Pose Estimation ◽  
3D Object ◽  
Occluding Contours ◽  
Model Based ◽  
Object Pose Estimation
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