Implementing Face Detection System on UAV Using Raspberry Pi Platform

Author(s):  
H. Daryanavard ◽  
A. Harifi
2021 ◽  
Vol 4 (1) ◽  
pp. 67-77
Author(s):  
Fransiska Sisilia Mukti ◽  
Lia Farokhah ◽  
Nur Lailatul Aqromi

Bus is one of public transportation and as the most preferable by Indonesian to support their mobility. The high number of bus traffics then demands the bus management to provide the maximum service for their passenger, in order to gain public trust. Unfortunately, in the reality passenger list’s fraud is often faced by the bus management, there is a mismatch list between the amount of deposits made by bus driver and the number of passengers carried by the bus, and as the result it caused big loss for the Bus management. Automatic Passenger Counting (APC) then as an artificial intelligence program that is considered to cope with the bus management problems. This research carried out an APC technology based on passenger face detection using the Viola-Jones method, which is integrated with an embedded system based on the Internet of Things in the processing and data transmission. To detect passenger images, a webcam is provided that is connected to the Raspberry pi which is then sent to the server via the Internet to be displayed on the website provided. The system database will be updated within a certain period of time, or according to the stop of the bus (the system can be adjusted according to management needs). The system will calculate the number of passengers automatically; the bus management can export passenger data whenever as they want. There are 3 main points in the architecture of modeling system, they are information system design, device architecture design, and face detection mechanism design to calculate the number of passengers. A system design test is carried out to assess the suitability of the system being built with company needs. Then, based on the questionnaire distributed to the respondent, averagely 85.12 % claim that the Face detection system is suitability. The score attained from 4 main aspects including interactivity, aesthetics, layout and personalization


IJIREEICE ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 186-191
Author(s):  
Shrutika V. Deshmukh ◽  
Prof Dr. Kshirsagar U. A.

Face detection is an important process when it comes to computer vision. It will serve as an input to a Facial expression and Face recognition system. Modern “C.C.T.V” cameras with face detection features are costly and only few are connected to the internet. This paper proposes a Face detection system which detects faces with a fusion of Convolutional neural network and Gabor Filter. Gabor filter is used to extract important facial features and Convolutional neural network is used to train the model. Model weights files are executed in Raspberry PI which is cost efficient. Raspberry pi is connected to cloud service which will alert the user with SMS and E-mail.


2019 ◽  
Vol 8 (3) ◽  
pp. 2477-2481

Nowadays, crime incidents like stealing, fighting and harassment often occur in campus leading to serious consequences. Students do not feel secure to study in campus anymore. Thus, a simple facial emotion detection system using a Raspberry Pi is introduced to help mitigating the issue before getting worse in campus. Two algorithms are used for this project including Haar Cascade and Local Binary Pattern (LBP) algorithms. OpenCV is a library that can be used for image processing. LBP algorithm is used for face detection in OpenCV. When a person enters the specified area, the camera will capture the image and detect the image of the person. Then, a rectangular box appears on the face image of the person. The image is automatically sent to the email. The face detection is enhanced by adding a face alignment. The face alignment is used to detect the location of many points on the face. It recognizes the emotions for each face and gives the confidence score. The value 0 of confidence score is the perfect face recognition. Although the system is simple, it is still reliable to be used in a campus environment.


2019 ◽  
Vol 7 (6) ◽  
pp. 14-18
Author(s):  
Sumangala Biradar ◽  
Beena Torgal ◽  
Namrata Hosamani ◽  
Renuka Bidarakundi ◽  
Shruti Mudhol

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.


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