scholarly journals A Deep Learning-Based Approach to Enable Action Recognition for Construction Equipment

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jinyue Zhang ◽  
Lijun Zi ◽  
Yuexian Hou ◽  
Mingen Wang ◽  
Wenting Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Action Recognition ◽  
Short Term Memory ◽  
Human Action Recognition ◽  
Human Action ◽  
Image Features ◽  
Construction Equipment ◽  
Video Frame ◽  
Convolutional Network

In order to support smart construction, digital twin has been a well-recognized concept for virtually representing the physical facility. It is equally important to recognize human actions and the movement of construction equipment in virtual construction scenes. Compared to the extensive research on human action recognition (HAR) that can be applied to identify construction workers, research in the field of construction equipment action recognition (CEAR) is very limited, mainly due to the lack of available datasets with videos showing the actions of construction equipment. The contributions of this research are as follows: (1) the development of a comprehensive video dataset of 2,064 clips with five action types for excavators and dump trucks; (2) a new deep learning-based CEAR approach (known as a simplified temporal convolutional network or STCN) that combines a convolutional neural network (CNN) with long short-term memory (LSTM, an artificial recurrent neural network), where CNN is used to extract image features and LSTM is used to extract temporal features from video frame sequences; and (3) the comparison between this proposed new approach and a similar CEAR method and two of the best-performing HAR approaches, namely, three-dimensional (3D) convolutional networks (ConvNets) and two-stream ConvNets, to evaluate the performance of STCN and investigate the possibility of directly transferring HAR approaches to the field of CEAR.

scholarly journals Deep Learning for Human Action Recognition with Convolution Neural Network

2020 ◽  
pp. 376-380
Author(s):  
S. Karthickkumar ◽  
K. Kumar
Keyword(s):  
Neural Network ◽  
Health Care ◽  
Deep Learning ◽  
Action Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Human Action Recognition ◽  
Human Action ◽  
Two Dimensional ◽  
Consumer Behavior Analysis

In recent years, deep learning for human action recognition is one of the most popular researches. It has a variety of applications such as surveillance, health care, and consumer behavior analysis, robotics. In this paper to propose a Two-Dimensional (2D) Convolutional Neural Network for recognizing Human Activities. Here the WISDM dataset is used to tarin and test the data. It can have the Activities like sitting, standing and downstairs, upstairs, running. The human activity recognition performance of our 2D-CNN based method which shows 93.17% accuracy.

Real-Time Human Action Recognition Using Deep Learning Architecture

2021 ◽  
Author(s):  
Souhila Kahlouche ◽  
Mahmoud Belhocine ◽  
Abdallah Menouar
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Action Recognition ◽  
Spatial Data ◽  
Short Term Memory ◽  
Human Action Recognition ◽  
Human Action ◽  
Microsoft Kinect ◽  
Kinect Camera ◽  
Public Datasets

In this work, efficient human activity recognition (HAR) algorithm based on deep learning architecture is proposed to classify activities into seven different classes. In order to learn spatial and temporal features from only 3D skeleton data captured from a “Microsoft Kinect” camera, the proposed algorithm combines both convolution neural network (CNN) and long short-term memory (LSTM) architectures. This combination allows taking advantage of LSTM in modeling temporal data and of CNN in modeling spatial data. The captured skeleton sequences are used to create a specific dataset of interactive activities; these data are then transformed according to a view invariant and a symmetry criterion. To demonstrate the effectiveness of the developed algorithm, it has been tested on several public datasets and it has achieved and sometimes has overcome state-of-the-art performance. In order to verify the uncertainty of the proposed algorithm, some tools are provided and discussed to ensure its efficiency for continuous human action recognition in real time.

scholarly journals Remarkable Skeleton Based Human Action Recognition

2020 ◽  
pp. 109-122
Author(s):  
Sushma Jaiswal ◽  
Tarun Jaiswal
Keyword(s):  
Deep Learning ◽  
Cognitive Science ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Frame ◽  
Learning Method ◽  
Long Time ◽  
3D Skeleton

Skeleton-based human-action-recognition (SBHAR) has wide applications in cognitive science and automatic surveillance. However, the most challenging and crucial task of the skeleton-based human-action-recognition (SBHAR) is a significant view variation while capturing the data. In this area, a significant amount of satisfactory work has already been done, which include the Red Green Blue (RGB) data method. The performance of the SBHAR is also affected by the various factors such as video frame setting, view variations in motion, different backgrounds and inter-personal differences. In this survey, we explicitly address these challenges and provide a complete overview of advancement in this field. The deep learning method has been used in this field for a long time, but so far, no research has fully demonstrated its usefulness. In this paper, we first highlight the need for action recognition and significance of 3D skeleton data and finally, we survey the largest 3D skeleton dataset, i.e. NTU-RGB+D and its new version NTU-RGB+D 120.

scholarly journals RECOGNITION OF HUMAN ACTIONS IN VIDEO SEQUENCES USING THE LSTM NETWORK

2021 ◽  
pp. 12-21
Author(s):  
A. R. Teplyakova ◽  
S. O. Starkov
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Action Recognition ◽  
Short Term Memory ◽  
Social Institutions ◽  
Human Action Recognition ◽  
The Elderly ◽  
Human Action ◽  
Recognition Problem ◽  
Software Module

The development of computer vision and the wide applicability of its applied components determine the relevance of research in this field of science. One of the most interesting tasks of computer vision is to monitor the behavior of people, which includes the analysis of their actions and carried out for various purposes. Examples of use are systems for monitoring compliance with safety regulations and the wearing of personal protective equipment by workers in factories, systems such as “smart home”, which track actions, systems for monitoring the condition of people in medical or social institutions, home systems for monitoring the condition of the elderly, which are able to notify relatives in cases of emergency situations. There is no comprehensive program that can solve the described problem and its variations, regardless of the scope of application. Therefore, the development of its prototype, which is a module that solves the human action recognition problem in the video, is an important problem. The article describes the creation of a software module that solves the human action recognition problem in a video. An overview of existing data sets suitable for training a neural network is provided, and data collection and processing for a custom dataset for actions of four different classes is described. The key features of the stages of creating, training and testing a neural network with the LSTM (Long Short-Term Memory) architecture, as well as options for its practical application, are described below. The developed module is quite flexible, there is a possibility to increase the number of classes of recognized actions depending on the scope of its application, as well as the possibility of integration with other modules for monitoring the behavior of people who have a similar device.

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

scholarly journals Study on Machine Learning and Deep Learning Methods for Human Action Recognition

2020 ◽  
Author(s):  
Gopika Rajendran ◽  
Ojus Thomas Lee ◽  
Arya Gopi ◽  
Jais jose ◽  
Neha Gautham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Action Recognition ◽  
Human Body ◽  
Human Performance ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Robot Interaction ◽  
Learning Approach ◽  
Action Sequence

With the evolution of computing technology in many application like human robot interaction, human computer interaction and health-care system, 3D human body models and their dynamic motions has gained popularity. Human performance accompanies human body shapes and their relative motions. Research on human activity recognition is structured around how the complex movement of a human body is identified and analyzed. Vision based action recognition from video is such kind of tasks where actions are inferred by observing the complete set of action sequence performed by human. Many techniques have been revised over the recent decades in order to develop a robust as well as effective framework for action recognition. In this survey, we summarize recent advances in human action recognition, namely the machine learning approach, deep learning approach and evaluation of these approaches.

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