Real-time isolated hand sign language recognition using deep networks and SVD
One of the challenges in computer vision models, especially sign language, is real-time
recognition. In this work, we present a simple yet low-complex and efficient model …
recognition. In this work, we present a simple yet low-complex and efficient model …
Action quality assessment using siamese network-based deep metric learning
Automated vision-based score estimation models can be used to provide an alternate
opinion to avoid judgment bias. Existing works have learned score estimation models by …
opinion to avoid judgment bias. Existing works have learned score estimation models by …
Human action recognition from digital videos based on deep learning
With the development of closed-circuit television, video-based human motion recognition
has made great progress. A large number of surveillance video footages have been …
has made great progress. A large number of surveillance video footages have been …
A Survey on Human Action Recognition based on Attention Mechanism
H Tang, J Cai - Proceedings of the 2022 7th International Conference …, 2022 - dl.acm.org
Human action recognition has always been an important challenge in the field of computer
vision. Human action recognition has a good development prospect for technology …
vision. Human action recognition has a good development prospect for technology …
Switching structured prediction for simple and complex human activity recognition
Automatic human activity recognition is an integral part of any interactive application
involving humans (eg, human–robot interaction systems). One of the main challenges for …
involving humans (eg, human–robot interaction systems). One of the main challenges for …
Data-level information enhancement: Motion-patch-based Siamese Convolutional Neural Networks for human activity recognition in videos
Data augmentation is critical for deep learning-based human activity recognition (HAR)
systems. However, conventional data augmentation methods, such as random-cropping …
systems. However, conventional data augmentation methods, such as random-cropping …
A graph convolutional siamese network for the assessment and recognition of physical rehabilitation exercises
C Li, X Ling, S Xia - International Conference on Artificial Neural Networks, 2023 - Springer
Recently, due to the attention of physical rehabilitation improves markedly, several
researchers attempt to implement automatic rehabilitation exercise analysis. However, most …
researchers attempt to implement automatic rehabilitation exercise analysis. However, most …
[PDF][PDF] 3D Human Activity Classification with 3D Zernike Moment Based Convolutional, LSTM-Deep Neural Networks.
Accepted: 8 March 2021 In this paper, we propose a method for classification 3D human
activities using the complementarity of CNNs, LSTMs, and DNNs by combining them into …
activities using the complementarity of CNNs, LSTMs, and DNNs by combining them into …
UWB radar traffic gesture recognition based on range-Doppler dual-channel fusion visual transformer network
Z Xiong, J Zhang, J Yin, G Xiong - Proceedings of the 2024 8th …, 2024 - dl.acm.org
In this paper, we introduce a novel traffic gesture recognition method based on a Dual-
Channel Fusion Visual Transformer (DCF-ViT) using UWB radar, to address the challenge of …
Channel Fusion Visual Transformer (DCF-ViT) using UWB radar, to address the challenge of …
A Combination Model of Shifting Joint Angle Changes With 3D-Deep Convolutional Neural Network to Recognize Human Activity
Research in the field of human activity recognition is very interesting due to its potential for
various applications such as in the field of medical rehabilitation. The need to advance its …
various applications such as in the field of medical rehabilitation. The need to advance its …