3-D Human Body Posture Reconstruction by Computer Vision

Cruz-Silva, Jacobo E.; Montiel-Pérez, J. Yaljá; Azuela, Juan Humberto Sossa

doi:10.1007/978-3-030-33749-0_46

Cited by 2 publications

(3 citation statements)

References 28 publications

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“…The design goal was to show that a limited number of critical poses are adequate to clarify and acknowledge a person's behaviour. Srijanet (Das et al, 2018), Cruz (Cruz-Silva et al, 2019 and Khaire (Khaire et al, 2018) designed and created a technique that supported skeleton and discourse function extraction in tandem. The RGB-D device, as well as the CNN and LSTM models were used to extract skeleton options.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

Review on recent Computer Vision Methods for Human Action Recognition

Muhamada

Mohammed

2022

ADCAIJ

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“…The purpose of this architecture was to select and demonstrate that the minimum number of key poses is sufficient to describe and recognize a human activity. Srijanet al, 2018 [31], Cruz et al [32], and Khaire et al [33] proposed and developed a method based on the combination of skeleton and contextual feature extraction. The skeleton features were extracted by the RGB-D sensor and the CNN and LSTM models.…”

Section: Related Workmentioning

confidence: 99%

“…State-of-the-art methods and their interpretation. Cont.Srijan et al, 2018[31], Cruz et al[32] and Khaire et al[33] RGB-D + CNN + LSTM model…”

mentioning

confidence: 99%

Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model

Jaouedi¹,

Perales

Buades

et al. 2020

Sensors

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The recognition of human activities is usually considered to be a simple procedure. Problems occur in complex scenes involving high speeds. Activity prediction using Artificial Intelligence (AI) by numerical analysis has attracted the attention of several researchers. Human activities are an important challenge in various fields. There are many great applications in this area, including smart homes, assistive robotics, human–computer interactions, and improvements in protection in several areas such as security, transport, education, and medicine through the control of falling or aiding in medication consumption for elderly people. The advanced enhancement and success of deep learning techniques in various computer vision applications encourage the use of these methods in video processing. The human presentation is an important challenge in the analysis of human behavior through activity. A person in a video sequence can be described by their motion, skeleton, and/or spatial characteristics. In this paper, we present a novel approach to human activity recognition from videos using the Recurrent Neural Network (RNN) for activity classification and the Convolutional Neural Network (CNN) with a new structure of the human skeleton to carry out feature presentation. The aims of this work are to improve the human presentation through the collection of different features and the exploitation of the new RNN structure for activities. The performance of the proposed approach is evaluated by the RGB-D sensor dataset CAD-60. The experimental results show the performance of the proposed approach through the average error rate obtained (4.5%).

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3-D Human Body Posture Reconstruction by Computer Vision

Cited by 2 publications

References 28 publications

Review on recent Computer Vision Methods for Human Action Recognition

Review on recent Computer Vision Methods for Human Action Recognition

Prediction of Human Activities Based on a New Structure of Skeleton Features and Deep Learning Model

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