Review of dynamic gesture recognition
SHI Yuanyuan, LI Yunan, FU Xiaolong, M Kaibin… - Virtual Reality & …, 2021 - Elsevier
In recent years, gesture recognition has been widely used in the fields of intelligent driving,
virtual reality, and human-computer interaction. With the development of artificial …
virtual reality, and human-computer interaction. With the development of artificial …
Hand gesture recognition in automotive human–machine interaction using depth cameras
N Zengeler, T Kopinski, U Handmann - Sensors, 2018 - mdpi.com
In this review, we describe current Machine Learning approaches to hand gesture
recognition with depth data from time-of-flight sensors. In particular, we summarise the …
recognition with depth data from time-of-flight sensors. In particular, we summarise the …
MMTM: Multimodal transfer module for CNN fusion
In late fusion, each modality is processed in a separate unimodal Convolutional Neural
Network (CNN) stream and the scores of each modality are fused at the end. Due to its …
Network (CNN) stream and the scores of each modality are fused at the end. Due to its …
Real-time hand gesture detection and classification using convolutional neural networks
Real-time recognition of dynamic hand gestures from video streams is a challenging task
since (i) there is no indication when a gesture starts and ends in the video,(ii) performed …
since (i) there is no indication when a gesture starts and ends in the video,(ii) performed …
Audio-visual speech and gesture recognition by sensors of mobile devices
Audio-visual speech recognition (AVSR) is one of the most promising solutions for reliable
speech recognition, particularly when audio is corrupted by noise. Additional visual …
speech recognition, particularly when audio is corrupted by noise. Additional visual …
Searching multi-rate and multi-modal temporal enhanced networks for gesture recognition
Gesture recognition has attracted considerable attention owing to its great potential in
applications. Although the great progress has been made recently in multi-modal learning …
applications. Although the great progress has been made recently in multi-modal learning …
Towards domain-independent and real-time gesture recognition using mmwave signal
Human gesture recognition using millimeter-wave (mmWave) signals provides attractive
applications including smart home and in-car interfaces. While existing works achieve …
applications including smart home and in-car interfaces. While existing works achieve …
An efficient pointlstm for point clouds based gesture recognition
Point clouds contain rich spatial information, which provides complementary cues for
gesture recognition. In this paper, we formulate gesture recognition as an irregular …
gesture recognition. In this paper, we formulate gesture recognition as an irregular …
MultiD-CNN: A multi-dimensional feature learning approach based on deep convolutional networks for gesture recognition in RGB-D image sequences
Human gesture recognition has become a pillar of today's intelligent Human-Computer
Interfaces as it typically provides more comfortable and ubiquitous interaction. Such expert …
Interfaces as it typically provides more comfortable and ubiquitous interaction. Such expert …
Attention in convolutional LSTM for gesture recognition
Convolutional long short-term memory (LSTM) networks have been widely used for
action/gesture recognition, and different attention mechanisms have also been embedded …
action/gesture recognition, and different attention mechanisms have also been embedded …