Review of Human Gesture Recognition Based on Computer Vision Technology

Zhao, Ran; Wang, Yan; Jia, Ping; Li, Cuiling; Ma, Yunlong; Zhang, Zhishu

doi:10.1109/iaeac50856.2021.9390889

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…III-C) are implemented on four published multi-view datasets of MICAGes, IXMAS, MuHAVi and NUMA, as illustrated in the IV. In this work, we train the end-to-end CNN models with two strategies: (1) Training on the remaining (N-1) viewpoints and testing on one viewpoint;…”

Section: The Experimental Results Inmentioning

confidence: 99%

“…Human gesture recognition is an attractive field in computer vision with many applications such as human computer interaction, human behavior analysis, intelligent surveillance, and virtual reality [1], [2]. A recognition system could use (1) static gestures and (2) dynamic gestures. In comparison with static gesture recognition, dynamic recognition is much more challenging.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a novel end-to-end system is proposed for (1) generating synthetic videos at multiple fixed viewpoints from a real dynamic gesture at an arbitrary viewpoint, and (2) classifying dynamic gestures from multi-view synthetic dynamic gestures. The proposed system contains two main components, and each is responsible for a certain task as follows:…”

Section: Introductionmentioning

confidence: 99%

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An End-to-End Model of ArVi-MoCoGAN and C3D with Attention Unit for Arbitrary-view Dynamic Gesture Recognition

Doan,

Luong,

Pham

2024

IJACSA

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Human gesture recognition is an attractive research area in computer vision with many applications such as humanmachine interaction, virtual reality, etc. Recent deep learning techniques have been efficiently applied for gesture recognition, but they require a large and diverse amount of training data. In fact, the available gesture datasets contain mostly static gestures and/or certain fixed viewpoints. Some contain dynamic gestures, but they are not diverse in poses and viewpoints. In this paper, we propose a novel end-to-end framework for dynamic gesture recognition from unknown viewpoints. It has two main components: (1) an efficient GAN-based architecture, named ArVi-MoCoGAN; (2) the gesture recognition component, which contains C3D backbones and an attention unit. ArVi-MoCoGAN aims at generating videos at multiple fixed viewpoints from a real dynamic gesture at an arbitrary viewpoint. It also returns the probability that a real arbitrary view gesture belongs to which of the fixed-viewpoint gestures. These outputs of ArVi-MoCoGAN will be processed in the next component to improve the arbitrary view recognition performance through multi-view synthetic gestures. The proposed system is extensively analyzed and evaluated on four standard dynamic gesture datasets. The experimental results of our proposed method are better than the current solutions, from 1% to 13.58% for arbitrary view gesture recognition and from 1.2% to 7.8% for single view gesture recognition.

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Section: The Experimental Results Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An End-to-End Model of ArVi-MoCoGAN and C3D with Attention Unit for Arbitrary-view Dynamic Gesture Recognition

Doan,

Luong,

Pham

2024

IJACSA

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“…Optical flow is a significant means of kinematics analysis in computer vision, widely used in dynamic detection tasks, including tracking [37], gesture recognition [38], etc. By finding the correlation between two frames of continuous signals, the algorithm can find the corresponding relationship of each pixel to estimate the movement of objects [20,39].…”

Section: B Background Of Optical Flow Methodsmentioning

confidence: 99%

Static-Dynamic Temporal Networks for Parkinson’s Disease Detection and Severity Prediction

Dong

Chen

Huan

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Most patients with Parkinson's disease (PD) have different degrees of movement disorders, and effective gait analysis has a huge potential for uncovering hidden gait patterns to achieve the diagnosis of patients with PD. In this paper, the Static-Dynamic temporal networks are proposed for gait analysis. Our model involves a Static temporal pathway and a Dynamic temporal pathway. In the Static temporal pathway, the time series information of each sensor is processed independently with a parallel one-dimension convolutional neural network (1D-Convnet) to extract respective depth features. In the Dynamic temporal pathway, the stitched surface of the feet is deemed to be an irregular "image", and the transfer of the force points at all levels on the sole is regarded as the "optical flow." Then, the motion information of the force points at all levels is extracted by 16 parallel two-dimension convolutional neural network (2D-Convnet) independently. The results show that the Static-Dynamic temporal networks achieved better performance in gait detection of PD patients than other previous methods. Among them, the accuracy of PD diagnosis reached 96.7%, and the accuracy of severity prediction of PD reached 92.3%.

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An Integrated Analysis for Identifying Iconic Gestures in Human-Robot Interactions

Singh,

Bansal

2024

Lecture Notes in Networks and Systems

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Review of Human Gesture Recognition Based on Computer Vision Technology

Cited by 9 publications

References 10 publications

An End-to-End Model of ArVi-MoCoGAN and C3D with Attention Unit for Arbitrary-view Dynamic Gesture Recognition

An End-to-End Model of ArVi-MoCoGAN and C3D with Attention Unit for Arbitrary-view Dynamic Gesture Recognition

Static-Dynamic Temporal Networks for Parkinson’s Disease Detection and Severity Prediction

An Integrated Analysis for Identifying Iconic Gestures in Human-Robot Interactions

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