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.

scholarly journals Human Actions and Hand Gesture Recognition with Deep Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 1250-1253
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Gesture Recognition ◽  
Human Activities ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Characteristic Features

Over recent times, deep learning has been challenged extensively to automatically read and interpret characteristic features from large volumes of data. Human Action Recognition (HAR) has been experimented with variety of techniques like wearable devices, mobile devices etc., but they can cause unnecessary discomfort to people especially elderly and child. Since it is very vital to monitor the movements of elderly and children in unattended scenarios, thus, HAR is focused. A smart human action recognition method to automatically identify the human activities from skeletal joint motions and combines the competencies are focused. We can also intimate the near ones about the status of the people. Also, it is a low-cost method and has high accuracy. Thus, this provides a way to help the senior citizens and children from any kind of mishaps and health issues. Hand gesture recognition is also discussed along with human activities using deep learning.

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 ASNet: Auto-Augmented Siamese Neural Network for Action Recognition

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4720
Author(s):  
Yujia Zhang ◽  
Lai-Man Po ◽  
Jingjing Xiong ◽  
Yasar Abbas Ur REHMAN ◽  
Kwok-Wai Cheung
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Data Augmentation ◽  
Recognition Performance ◽  
Human Action Recognition ◽  
Human Action ◽  
Deep Convolutional Neural Networks ◽  
Learning Agent ◽  
Markov Decision ◽  
The Impact

Human action recognition methods in videos based on deep convolutional neural networks usually use random cropping or its variants for data augmentation. However, this traditional data augmentation approach may generate many non-informative samples (video patches covering only a small part of the foreground or only the background) that are not related to a specific action. These samples can be regarded as noisy samples with incorrect labels, which reduces the overall action recognition performance. In this paper, we attempt to mitigate the impact of noisy samples by proposing an Auto-augmented Siamese Neural Network (ASNet). In this framework, we propose backpropagating salient patches and randomly cropped samples in the same iteration to perform gradient compensation to alleviate the adverse gradient effects of non-informative samples. Salient patches refer to the samples containing critical information for human action recognition. The generation of salient patches is formulated as a Markov decision process, and a reinforcement learning agent called SPA (Salient Patch Agent) is introduced to extract patches in a weakly supervised manner without extra labels. Extensive experiments were conducted on two well-known datasets UCF-101 and HMDB-51 to verify the effectiveness of the proposed SPA and ASNet.

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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