scholarly journals Human Detection in Video Surveillance

2021 ◽  
Vol 03 (01) ◽  
pp. 27-34
Author(s):  
Sushama Khanvilkar ◽  
◽  
Santosh Gupta ◽  
Hinal Rane ◽  
Calvin Galbaw ◽  
...  
Keyword(s):  
Deep Learning ◽  
Video Surveillance ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Human Detection ◽  
Fast Computation ◽  
Intelligent Surveillance ◽  
Fully Connected ◽  
Deep Learning Model ◽  
Naive Approach

Recognition of the human activities in videos has gathered numerous demands in various applications of computer vision like Ambient Assisted Living, intelligent surveillance, Human-Computer interaction. One of the most pioneering techniques for Human Detection in Video Surveillance based on deep learning and this project mainly focuses on various approaches based on that. This paper provides an idea of solution to use video surveillance more effectively, by detecting any humans present and notifying the concerned people. The deep learning model, preferred for fast computation, Convolution Neural Network is used by stacking 3 blocks of layers on fully connected layers. This provided an identification of humans and naïve approach to eliminate inanimate human like objects such as mannequins.

scholarly journals Vision based Patient Fall Detection using Deep Learning in Smart Hospitals

2019 ◽  
Vol 9 (2) ◽  
pp. 4826-4832
Keyword(s):  
Deep Learning ◽  
Video Surveillance ◽  
Fall Detection ◽  
Hospital Staff ◽  
Comparison Study ◽  
New Concepts ◽  
Surveillance Cameras ◽  
Time Fall ◽  
Smart Hospitals ◽  
Deep Learning Model

With the emergence of new concepts like smart hospitals, video surveillance cameras should be introduced in each room of the hospital for the purpose of safety and security. These surveillance cameras can also be used to provide assistance to patients and hospital staff. In particular, a real-time fall of a patient can be detected with the help of these cameras and accordingly, assistance can be provided to them. Different models have already been developed by researchers to detect a human fall using a camera. This paper proposes a vision based deep learning model to detect a human fall. Along with this model, two mathematical based models have also been proposed which uses pre-trained YOLO FCNN and Faster R-CNN architecture to detect the human fall. At the end of this paper, a comparison study has been done on these models to specify which method provides the most accurate results

Forecasting Trends in an Ambient Assisted Living Environment Using Deep Learning

2021 ◽  
Author(s):  
Guillaume Gingras ◽  
Mehdi Adda ◽  
Abdenour Bouzouane ◽  
Hussein Ibrahim ◽  
Clemence Dallaire
Keyword(s):  
Deep Learning ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Living Environment

An Effective Video Surveillance System by using CNN for COVID-19

2022 ◽  
pp. 88-102
Author(s):  
Basetty Mallikarjuna ◽  
Anusha D. J. ◽  
Sethu Ram M. ◽  
Munish Sabharwal
Keyword(s):  
Deep Learning ◽  
Video Surveillance ◽  
Surveillance System ◽  
Video Surveillance System ◽  
Learning Technology ◽  
Proposed Model ◽  
The Social ◽  
Scripting Language ◽  
Python Programming ◽  
Fully Connected

An effective video surveillance system is a challenging task in the COVID-19 pandemic. Building a model proper way of wearing a mask and maintaining the social distance minimum six feet or one or two meters by using CNN approach in the COVID-19 pandemic, the video surveillance system works with the help of TensorFlow, Keras, Pandas, which are libraries used in Python programming scripting language used in the concepts of deep learning technology. The proposed model improved the CNN approach in the area of deep learning and named as the Ram-Laxman algorithm. The proposed model proved to build the optimized approach, the convolutional layers grouped as ‘Ram', and fully connected layers grouped as ‘Laxman'. The proposed system results convey that the Ram-Laxman model is easy to implement in the CCTV footage.

scholarly journals Ambient Assisted Living: A Research on Human Activity Recognition and Vital Health Sign Monitoring using Deep Learning Approaches

2019 ◽  
Vol 8 (6S4) ◽  
pp. 531-540
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Independent Living ◽  
Ambient Assisted Living ◽  
The Elderly ◽  
Human Activity Recognition ◽  
Living Environment ◽  
Learning Approaches

The rise in life expectancy rate and dwindled birth rate in new age society has led to the phenomenon of population ageing which is being witnessed across the world from past few decades. India is also a part of this demographic transition which will have the direct impact on the societal and economic conditions of the country. In order to effectively deal with the prevailing phenomenon, stakeholders involved are coming up with the Information and Communication Technology (ICT) based ecosystem to address the needs of elderly people such as independent living, activity recognition, vital health sign monitoring, prevention from social isolation etc. Ambient Assisted Living (AAL) is one such ecosystem which is capable of providing safe and secured living environment for the elderly and disabled people. In this paper we will focus on reviewing the sensor based Human Activity Recognition (HAR) and Vital Health Sign Monitoring (VHSM) which is applicable for AAL environments. At first we generally describe the AAL environment. Next we present brief insights into sensor modalities and different deep learning architectures. Later, we survey the existing literature for HAR and VHSM based on sensor modality and deep learning approach used.

scholarly journals Deep learning-based ambient assisted living for self-management of cardiovascular conditions

2021 ◽  
Author(s):  
Maria Ahmed Qureshi ◽  
Kashif Naseer Qureshi ◽  
Gwanggil Jeon ◽  
Francesco Piccialli
Keyword(s):  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Learning Strategies ◽  
Assisted Living ◽  
New Technologies ◽  
Ambient Assisted Living ◽  
World Health ◽  
Self Management ◽  
Ageing Population ◽  
Cloud Models

AbstractAccording to the World Health Organization, cardiovascular diseases contribute to 17.7 million deaths per year and are rising with a growing ageing population. In order to handle these challenges, the evolved countries are now evolving workable solutions based on new communication technologies such as ambient assisted living. In these solutions, the most well-known solutions are wearable devices for patient monitoring, telemedicine and mHealth systems. This systematic literature review presents the detailed literature on ambient assisted living solutions and helps to understand how ambient assisted living helps and motivates patients with cardiovascular diseases for self-management to reduce associated morbidity and mortalities. Preferred reporting items for systematic reviews and meta-analyses technique are used to answer the research questions. The paper is divided into four main themes, including self-monitoring wearable systems, ambient assisted living in aged populations, clinician management systems and deep learning-based systems for cardiovascular diagnosis. For each theme, a detailed investigation shows (1) how these new technologies are nowadays integrated into diagnostic systems and (2) how new technologies like IoT sensors, cloud models, machine and deep learning strategies can be used to improve the medical services. This study helps to identify the strengths and weaknesses of novel ambient assisted living environments for medical applications. Besides, this review assists in reducing the dependence on caregivers and the healthcare systems.

scholarly journals A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment

2021 ◽  
Vol 11 (4) ◽  
pp. 1564
Author(s):  
SeonWoo Lee ◽  
HyeonTak Yu ◽  
HoJun Yang ◽  
InSeo Song ◽  
JungMu Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Vibration Signal ◽  
Short Time Fourier Transform ◽  
Mel Frequency Cepstral Coefficients ◽  
Vibration Data ◽  
Amplitude Data ◽  
Gravity Acceleration ◽  
Short Time ◽  
Fully Connected ◽  
Deep Learning Model

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accelerometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The method proposed in this paper was trained with transfer learning, a deep learning model that replaced the VGG19 model with a Fully Connected Layer (FCL) and Global Average Pooling (GAP) by converting the vibration signal into a short-time Fourier transform (STFT) or Mel-Frequency Cepstral Coefficients (MFCC) spectrogram and converting the input into a 2D image. As a result, the model proposed in this paper has seven times decreased trainable parameters of VGG19, and it is possible to quantify the severity while looking at the defect areas that cannot be seen with 1D.

scholarly journals Graph Transformer for drug response prediction

2021 ◽  
Author(s):  
Tuan Thanh Nguyen ◽  
Thang Chu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Drug Response ◽  
State Of The Art ◽  
Response Prediction ◽  
Graph Representation ◽  
Omics Data ◽  
Information Redundancy ◽  
Fully Connected ◽  
Deep Learning Model

Previous models have shown that learning drug features from their graph representation is more efficient than learning from their strings or numeric representations. Furthermore, integrating multi-omics data of cell lines increases the performance of drug response prediction. However, these models showed drawbacks in extracting drug features from graph representation and incorporating redundancy information from multi-omics data. This paper proposes a deep learning model, GraTransDRP, to better drug representation and reduce information redundancy. First, the Graph transformer was utilized to extract the drug representation more efficiently. Next, Convolutional neural networks were used to learn the mutation, meth, and transcriptomics features. However, the dimension of transcriptomics features is up to 17737. Therefore, KernelPCA was applied to transcriptomics features to reduce the dimension and transform them into a dense presentation before putting them through the CNN model. Finally, drug and omics features were combined to predict a response value by a fully connected network. Experimental results show that our model outperforms some state-of-the-art methods, including GraphDRP, GraOmicDRP.

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