Hand Gesture Recognition for Real Time Human Machine Interaction System

Sonwalkar, Poonam; Sakhare, Tanuja; Patil, Ashwini R.; Kale, Sonal

doi:10.14445/22315381/ijett-v19p245

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…Specific actions can only be done as a result of static images (Sonwalkar et al, 2015). While despite the fact that the temporal context plays a crucial role in making (Singh et al, 2019) however that has received less attention than the recognition of images, it is still possible to identify video datasets, as shown in table 4.…”

Section: Image Datasetsmentioning

confidence: 99%

“…While despite the fact that the temporal context plays a crucial role in making (Singh et al, 2019) however that has received less attention than the recognition of images, it is still possible to identify video datasets, as shown in table 4. (Delaitre et al, 2010) Table 4: Evaluation of images datasets (Singh et al, 2019) Stanford forty actions (Sonwalkar et al, 2015) is a picture dataset of forty human actions occurring daily gathered by search queries on Google, Bing, and Flickr. As many as 9,352 pictures exist, each with bounding boxes for the person acting in it.…”

Section: Image Datasetsmentioning

confidence: 99%

“…Willow (2010) DatasetThis dataset(Sonwalkar et al, 2015) is a 968-picture still image dataset of typical human behavior culled from Flickr mistreatment search queries after manual filtering. The dataset is divided into seven categories, including Computer Interaction, Horse Riding, and Walking.…”

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confidence: 99%

“…Figure 7: A some example classes from Stanford forty actions dataset 6.3. TUHOI (2014)The metropolis The Universal Human Object Interaction Dataset(Ballas et al, 2015) includes the interaction between human and object and includes 189 typical objects in ten, 805 images of DET dataset in the ImageNet 2013 contests(Sonwalkar et 2015). Mistreatment is annotated for 2974 different activities in the form "verb + object," which is a crowdsourcing program called Crowd flower.…”

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confidence: 99%

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Review on recent Computer Vision Methods for Human Action Recognition

Muhamada

Mohammed

2022

ADCAIJ

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The subject of human activity recognition is considered an important goal in the domain of computer vision from the beginning of its development and has reached new levels. It is also thought of as a simple procedure. Problems arise in fast-moving and advanced scenes, and the numerical analysis of artificial intelligence (AI) through activity prediction mistreatment increased the attention of researchers to study. Having decent methodological and content related variations, several datasets were created to address the evaluation of these ways. Human activities play an important role but with challenging characteristic in various fields. Many applications exist in this field, such as smart home, helpful AI, HCI (Human-Computer Interaction), advancements in protection in applications such as transportation, education, security, and medication management, including falling or helping elderly in medical drug consumption. The positive impact of deep learning techniques on many vision applications leads to deploying these ways in video processing. Analysis of human behavior activities involves major challenges when human presence is concerned. One individual can be represented in multiple video sequences through skeleton, motion and/or abstract characteristics. This work aims to address human presence by combining many options and utilizing a new RNN structure for activities. The paper focuses on recent advances in machine learning-assisted action recognition. Existing modern techniques for the recognition of actions and prediction similarly because the future scope for the analysis is mentioned accuracy within the review paper.

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Section: Image Datasetsmentioning

confidence: 99%

Section: Image Datasetsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Review on recent Computer Vision Methods for Human Action Recognition

Muhamada

Mohammed

2022

ADCAIJ

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“…The main goal of this area is to determine, and then predict what humans do in a video or a sequence of images. There are many potential applications such as intelligent surveillance systems [1], [2], [3], human-computer interfaces [4], [5], health care [6], virtual reality [7], or security and military applications [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Video-based Human Action Recognition using Deep Learning: A Review

Pham¹,

Khoudour²,

Crouzil³

et al. 2022

Preprint

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Human action recognition is an important application domain in computer vision. Its primary aim is to accurately describe human actions and their interactions from a previously unseen data sequence acquired by sensors. The ability to recognize, understand and predict complex human actions enables the construction of many important applications such as intelligent surveillance systems, human-computer interfaces, health care, security and military applications. In recent years, deep learning has been given particular attention by the computer vision community. This paper presents an overview of the current state-of-the-art in action recognition using video analysis with deep learning techniques. We present the most important deep learning models for recognizing human actions, analyze them to provide the current progress of deep learning algorithms applied to solve human action recognition problems in realistic videos highlighting their advantages and disadvantages. Based on the quantitative analysis using recognition accuracies reported in the literature, our study identifies state-of-the-art deep architectures in action recognition and then provides current trends and open problems for future works in this filed.

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Learning to recognise 3D human action from a new skeleton‐based representation using deep convolutional neural networks

Pham

Khoudour

Crouzil

et al. 2019

IET Computer Vision

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Recognizing human actions in untrimmed videos is an important challenging task. An effective 3D motion representation and a powerful learning model are two key factors influencing recognition performance. In this paper we introduce a new skeletonbased representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a color encoding process. By normalizing the 3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the color-coded representation is able to represent spatio-temporal evolutions of complex 3D motions, independently of the length of each sequence. We then design and train different Deep Convolutional Neural Networks (D-CNNs) based on the Residual Network architecture (ResNet) on the obtained image-based representations to learn 3D motion features and classify them into classes. Our method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches whilst requiring less computation for training and prediction.

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Hand Gesture Recognition for Real Time Human Machine Interaction System

Cited by 8 publications

References 14 publications

Review on recent Computer Vision Methods for Human Action Recognition

Review on recent Computer Vision Methods for Human Action Recognition

Video-based Human Action Recognition using Deep Learning: A Review

Learning to recognise 3D human action from a new skeleton‐based representation using deep convolutional neural networks

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