Author: Sun, Yuliang; Fei, Tai; Li, Xibo; Warnecke, Alexander; Warsitz, Ernst; Pohl, Nils
Title: Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform Cord-id: mtbo1tnq Document date: 2020_5_20
ID: mtbo1tnq
Snippet: In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based gesture recognition system, the proposed framework extracts a comprehensive hand profile, including range, Doppler, azimuth and elevation, over multiple measurement-cycles and encodes them into a feature cube. Rather than feeding the range-Doppler spectrum sequence into a deep con
Document: In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based gesture recognition system, the proposed framework extracts a comprehensive hand profile, including range, Doppler, azimuth and elevation, over multiple measurement-cycles and encodes them into a feature cube. Rather than feeding the range-Doppler spectrum sequence into a deep convolutional neural network (CNN) connected with recurrent neural networks, the proposed framework takes the aforementioned feature cube as input of a shallow CNN for gesture recognition to reduce the computational complexity. In addition, we develop a hand activity detection (HAD) algorithm to automatize the detection of gestures in real-time case. The proposed HAD can capture the time-stamp at which a gesture finishes and feeds the hand profile of all the relevant measurement-cycles before this time-stamp into the CNN with low latency. Since the proposed framework is able to detect and classify gestures at limited computational cost, it could be deployed in an edge-computing platform for real-time applications, whose performance is notedly inferior to a state-of-the-art personal computer. The experimental results show that the proposed framework has the capability of classifying 12 gestures in real-time with a high F1-score.
Search related documents:
Co phrase search for related documents- activation function and long lstm short term memory: 1, 2
- activation function and long lstm short term memory cnn: 1
- activation function and long short: 1, 2
- activation function and lstm short term memory: 1, 2
- activity detection and long lstm short term memory: 1, 2
- activity detection and long lstm short term memory cnn: 1
- activity detection and long short: 1, 2
- activity detection and lstm short term memory: 1, 2
- adam optimizer and long lstm short term memory: 1, 2
- adam optimizer and long short: 1, 2, 3
- adam optimizer and lstm layer: 1
- adam optimizer and lstm short term memory: 1, 2
- long lstm short term memory and lstm layer: 1, 2, 3, 4, 5, 6
- long lstm short term memory and lstm short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73
- long lstm short term memory cnn and lstm short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
Co phrase search for related documents, hyperlinks ordered by date