AuFloat (Autonomous Float) Based-on Artificial Inteligent and LORA (Long Range) Using Haar Cascade Method for Rescuing of Water Accident Victims

2019 ◽  
Author(s):  
Ahmad Ilham ◽  
Rachmad Tri Soelistijono ◽  
Aang Wahidin ◽  
Fathulloh ◽  
Gaguk Suhardjito ◽  
...  
Keyword(s):  
Long Range ◽  
Haar Cascade ◽  
Cascade Method ◽  
Water Accident

scholarly journals Facial Emotion Recognition for Autism Children

2020 ◽  
Vol 9 (7) ◽  
pp. 1274-1278
Keyword(s):  
Facial Expressions ◽  
Autism Spectrum ◽  
Facial Emotion ◽  
Expression Recognition ◽  
Emotion Detection ◽  
Age And Gender ◽  
Social Media Platforms ◽  
Haar Cascade ◽  
And Gender ◽  
Cascade Method

Nowadays Autism children find it difficult to interact socially with people emotions and make themselves isolated. This paper proposes Emotion detection for Autism spectrum disorder children (ASD). It is self-possessed of python libraries Open CV, Haar-cascade method and Age and gender prediction. Conversely, most existing methods rely on the detection of facial expressions of people in social media platforms such as snapchat use facial recognition technology and also detecting facial emotions from their Facial expressions in image. And for a better involvement of the children’s social behaviour, here a face is captured in real time and age, gender and emotions are predicted by Facial expression recognition (FER). This proposed system helps to improve the Autism children behaviour as they often observe the facial expressions of humans and try to imitate their emotions which make a huge difference in their behaviour.

scholarly journals Deep Neural Network for Better Face Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 2236-2239
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Detection ◽  
Deep Neural Network ◽  
Low Cost ◽  
Raspberry Pi ◽  
The Face ◽  
Frame Work ◽  
Haar Cascade ◽  
Cascade Method

This Paper represents the face detection using advanced method deep neural network which uses deep learning frame work. The old models used to detect the faces were like Haar-cascade method which detect the faces with good approaches but there is some uncertainty in the accuracy of the old models, so in this system we will use the latest deep neural network model which is embedded with latest open cv and by using the deep learning model frame work which is weighted with some other files. By using this model, we can achieve the better accuracy in face detection which can be used for further purposes like auto focus in cameras, counting number of people etc. This model detects the faces accurately and paves the way for better recognition systems which can be used in many face biometric applications. For this purpose, low-cost computer board Raspberry Pi and Camera Sensor will be used.

scholarly journals Face Recognition System for Prevention of Car Theft with Haar Cascade and Local Binary Pattern Histogram using Raspberry Pi

2020 ◽  
pp. 407-425
Author(s):  
Muhammad Hanif Abdurrahman ◽  
Haryadi Amran Darwito ◽  
Akuwan Saleh
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Haar Cascade ◽  
The Difference ◽  
Cascade Method ◽  
Car Security ◽  
Pattern Histogram

In this era, the occurrence of vehicle theft has become a fairly frequent problem, especially in big cities like Jakarta and Surabaya. Although the technology for car security is very sophisticated (e.g. keyless system), but there are many cases that thieves still can break into the system. Once a car was stolen, the whereabouts of the car was unknown and the thief was on the loose. The goal of this research is to overcome this problem. As a device, this research works on Raspberry Pi 3 that connected with the Raspberry Pi Camera. Using the OpenCV library, with the Haar Cascade method for face detection, and Local Binary Pattern Histogram for face recognition. The device must be connected to the internet in order to send the information using a Telegram message. The research results show the success of the device system in face-recognizing between the car owner and car thief with optimal conditions in the morning until the afternoon with the light intensity around 660 to 1000 lux, and best recognizing distance at 50 cm. The success rate for obtaining the car’s location for the outdoor condition is 100%. Even if there is a slope or an error data, it can be tolerated because the difference was not too high, about 0.1-1.0 m.

scholarly journals Smart attendance system using Convolution Neural Network and Image Processing

2020 ◽  
Vol 5 (5) ◽  
pp. 611-616
Author(s):  
Senigala Kuruba ChayaDevi ◽  
Vamsi Agnihotram
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Recognition ◽  
Imaging System ◽  
System Quality ◽  
Ann Model ◽  
Driver Assistance Systems ◽  
The Face ◽  
Haar Cascade ◽  
Cascade Method

Smart attendance maintenancesystem has been a research topic from past a fewdecades; each method has its own disadvantages and advantages.An algorithm using Convolutional Neural Network and Image processinghas been proposed in this paper to overcome the disadvantages of the previous algorithms. Image recognition is playing an important role in the modern living like driver assistance systems, medical imaging system, quality control system to name a few. An Artificial Neural Network along with image recognition used to enhance the reliability of the attendancesystem. One such update used here is CNN.Deep learning has been an emerging technology hence opted to implement the smart attendance system.The implementation basically consists of three components : 1)Face scanning and detection using HAAR cascade method 2)Training the CNN-ANN model 3)Recognize the face  and update the attendance .The main motivation of our work is to merge three of the emerging technologies : Machine learning , Image Processing and IOT . Key advantage of this implementation is that a deep learning model increases its accuracy with more epochs of training andit optimizes the run time.

scholarly journals Monitoring Driver’s Vigilance Level Using Real-Time Facial Expression and Deep Learning Techniques

2020 ◽  
Author(s):  
Arafat Al-Dweik ◽  
Reza Mohammadi Tamanani ◽  
Radu Muresan
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Human Error ◽  
Transportation Systems ◽  
Practical Applications ◽  
Facial Region ◽  
Learning Techniques ◽  
Haar Cascade ◽  
And Performance ◽  
Cascade Method

<div>Road accidents caused by human error are among</div><div>the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper,</div><div>a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method</div><div>for practical applications in intelligent transportation systems</div>

scholarly journals Monitoring Driver’s Vigilance Level Using Real-Time Facial Expression and Deep Learning Techniques

2020 ◽  
Author(s):  
Arafat Al-Dweik ◽  
Reza Mohammadi Tamanani ◽  
Radu Muresan
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Human Error ◽  
Transportation Systems ◽  
Practical Applications ◽  
Facial Region ◽  
Learning Techniques ◽  
Haar Cascade ◽  
And Performance ◽  
Cascade Method

<div>Road accidents caused by human error are among</div><div>the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper,</div><div>a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method</div><div>for practical applications in intelligent transportation systems</div>

scholarly journals Monitoring Driver’s Vigilance Level Using Real-Time Facial Expression and Deep Learning Techniques

2020 ◽  
Author(s):  
Arafat Al-Dweik ◽  
Reza Mohammadi Tamanani ◽  
Radu Muresan
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Human Error ◽  
Transportation Systems ◽  
Practical Applications ◽  
Facial Region ◽  
Learning Techniques ◽  
Haar Cascade ◽  
And Performance ◽  
Cascade Method

<div>Road accidents caused by human error are among</div><div>the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper,</div><div>a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method</div><div>for practical applications in intelligent transportation systems</div>

scholarly journals Face Tracking for Flying Robot Quadcopter based on Haar Cascade Classifier and PID Controller

2021 ◽  
Vol 2111 (1) ◽  
pp. 012046
Author(s):  
A S Priambodo ◽  
F Arifin ◽  
A Nasuha ◽  
A Winursito
Keyword(s):  
Programming Languages ◽  
Face Detection ◽  
Position Control ◽  
Detection System ◽  
Response Speed ◽  
Detection Algorithm ◽  
The Face ◽  
Haar Cascade ◽  
Cascade Method ◽  
The Right

Abstract The fundamental aim of this research is to develop a face detection system for a quadcopter in order to follow the face object. This research has two main stages, namely the face detection stage and the position control system. The face detection algorithm used in this research is the haar cascade method which is run using the python and OpenCV programming languages. The algorithm worked well, getting around 16fps on a low spec computer without a GPU unit. The results of the face detection algorithm are proven to be able to recognize faces from the camera installed on the DJI Tello mini drone. The mini drone was chosen because it is small and light, so it is harmless, and testing can be carried out indoors. Besides, the DJI Tello can be programmed easily using the python programming language. The drone’s position is then compared with the set point in the middle of the image to obtain errors so that control signals can be calculated for up/down, forward/backward, and right/left movements. From the testing results, the response speed that occurs in the right/left and up/down movements is less than 2 seconds, while for the forward/backward movement, it is less than 3 seconds.

scholarly journals Peningkatan Kinerja AMG8833 sebagai Thermocam dengan Metode Regresi AdaBoost untuk Pelaksanaan Protokol COVID-19

2021 ◽  
Vol 8 (1) ◽  
pp. 978
Author(s):  
Aziz Nurul Iman ◽  
Aji Gautama Putrada ◽  
Sidik Prabowo ◽  
Doan Perdana
Keyword(s):  
Body Temperature ◽  
Decision Tree ◽  
General Purpose ◽  
Test Results ◽  
Thermal Camera ◽  
Tree Model ◽  
Temperature Detection ◽  
Tree Models ◽  
Haar Cascade ◽  
Cascade Method

One way to prevent the spread of the COVID-19 virus is to check body temperature regularly. However, checking body temperature manually by directing the thermogun at someone's face is still often found. This study implements the use of the AMG8833 thermal camera to detect a person's body temperature without making any contact. The AMG8833 is a general-purpose temperature detection camera so to be used as a temperature meter, its accuracy needs to be improved by regression. The purpose of this research is to improve the performance of AMG833 as a thermal camera with AdaBoost regression. AdaBoost is a type of ensemble learning that uses several decision tree models. For face detection, the system uses the Haar Cascade method. The test results show that the decision tree model produces an R-Squared value of 0.93 and an RMSE of 0.21. Meanwhile, AdaBoost succeeded in improving the performance of the regression model with a higher R-Squared value and a lower RMSE value with values of 0.95 and 0.18, respectively.

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