scholarly journals A Comparison of Iterative 2D-3D Pose Estimation Methods for Real-Time Applications

2009 ◽  
pp. 706-715 ◽  
Author(s):  
Daniel Grest ◽  
Thomas Petersen ◽  
Volker Krüger
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Estimation Methods ◽  
3D Pose Estimation ◽  
Real Time Applications

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.

Analysis of Capacity Estimation Methods for Real-Time Applications in Internet Accesses

2008 ◽  
Author(s):  
Eduardo A. Viruete ◽  
Julian Fernandez-Navajas ◽  
Elena Macian-Senz ◽  
Ignacio Martinez ◽  
Rafael del-Hoyo ◽  
...  
Keyword(s):  
Real Time ◽  
Estimation Methods ◽  
Capacity Estimation ◽  
Real Time Applications

Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications

2014 ◽  
Vol 72 (10-11) ◽  
pp. 717-727 ◽  
Author(s):  
Davor Kukolja ◽  
Siniša Popović ◽  
Marko Horvat ◽  
Bernard Kovač ◽  
Krešimir Ćosić
Keyword(s):  
Comparative Analysis ◽  
Real Time ◽  
Estimation Methods ◽  
Real Time Applications ◽  
Emotion Estimation
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