Multi-Sensor Motion Fusion Using Deep Neural Network Learning

Hand pose estimation for a continuous sequence has been an important topic not only in computer vision but also human-computer-interaction. Exploring the feasibility to use hand gestures to replace input devices, e.g., mouse, keyboard, joy-stick and touch screen, has attracted increasing attention from academic and industrial researchers. The fast advancement of hand pose estimation techniques is complemented by the rapid development of smart sensors technology such as Kinect and Leap. We introduce a hand pose estimation multi-sensor system. Two tracking models are proposed based on Deep (Recurrent) Neural Network (DRNN) architecture. Data captured from different sensors are analyzed and fused to produce an optimal hand pose sequence. Experimental results show that our models outperform previous methods with better accuracy, meeting real-time application requirement. Performance comparisons between DNN and DRNN, spatial and spatial-temporal features, and single- and dual- sensors, are also presented.

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Multi-Sensor Motion Fusion Using Deep Neural Network Learning

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch032 ◽

2020 ◽

pp. 568-586

Author(s):

Xinyao Sun ◽

Anup Basu ◽

Irene Cheng

Keyword(s):

Neural Network ◽

Pose Estimation ◽

Rapid Development ◽

Neural Network Learning ◽

Input Devices ◽

Hand Pose Estimation ◽

Temporal Features ◽

Deep Recurrent Neural Network ◽

Real Time Application ◽

Hand Pose

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

Sensors ◽

10.3390/s20102828 ◽

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.

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