Analysis realization of Viola-Jones method for face detection on CCTV camera based on embedded system

2017 ◽  
Author(s):  
I. Gusti Ngurah Made Kris Raya ◽  
Agung Nugroho Jati ◽  
Randy Erfa Saputra
Keyword(s):  
Embedded System ◽  
Face Detection ◽  
Cctv Camera

scholarly journals PEMODELAN SISTEM DETEKSI WAJAH SEBAGAI PENGHITUNG JUMLAH PENUMPANG TRANSPORTASI PUBLIK

2021 ◽  
Vol 4 (1) ◽  
pp. 67-77
Author(s):  
Fransiska Sisilia Mukti ◽  
Lia Farokhah ◽  
Nur Lailatul Aqromi
Keyword(s):  
Embedded System ◽  
System Design ◽  
Face Detection ◽  
Public Transportation ◽  
Detection System ◽  
Raspberry Pi ◽  
The Internet ◽  
Detection Mechanism ◽  
Information System Design ◽  
The Face

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

A Realization of Face Detection System Based on ARM Linux

2013 ◽  
Vol 333-335 ◽  
pp. 864-867 ◽  
Author(s):  
Cong Ting Zhao ◽  
Hong Yun Wang ◽  
Jia Wei Li ◽  
Zi Lu Ying
Keyword(s):  
Embedded System ◽  
Monitoring System ◽  
Face Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Video Monitoring ◽  
Adaboost Algorithm ◽  
Arm Processor ◽  
The Face ◽  
Detection Effect

In order to adapt to the requirements of intelligent video monitoring system, this paper presents an ARM-Linux based video monitoring system for face detection. In this system, an ARM processor with a Linux operating system was used, and the USB camera was used to capture data, and then the face detection was conducted in the ARM device. The OpenCV library was transplanted to Linux embedded system. The algorithm of face detection was realized by calling the OpenCV library. Specially, adaboost algorithm was chose as the face detection algorithm. Experimental results show that the face detection effect of the system is satisfactory and can meet the real time requirement of video surveillance.

scholarly journals Real Time Face Detection using CCTV Camera

2019 ◽  
Vol 8 (6) ◽  
pp. 273-276
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Biometric Identification ◽  
Face Identification ◽  
Human Face ◽  
Human Face Detection ◽  
Detection Capabilities ◽  
Living Objects ◽  
Cctv Camera ◽  
Better Than

Closed Circuit Television i.e. CCTV are widely for security purposes getting the opportunity to be useful with time. Human face identification is one of major interests for this technology. In this paper, Human face is detection method is proposed with better accuracy and speed. This method can find wide use in this technological era as biometric identification is one of the best method of verification. In the proposed method divulgence of different facial parts, such as, Nose, Eyes & Mouth could be done effectively and rapidly, without being concerned of the light or illumination in the background of the person. For this we have used Ada Boost Algorithm through which quick and precise results have obtained that are far better than that of previous methods. The results presents critical improvement utilizing introduced technique over different past systems. It might be visible that proposed procedure is staggeringly able with basic spurring power in observation usage .This method find its extensive Human face detection capabilities for security purposes in identifying the person among the group of living or non-living objects.

scholarly journals ENHANCEMENT OF FAST FACE DETECTION ALGORITHM BASED ON A CASCADE OF DECISION TREES

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 237-241 ◽  
Author(s):  
V. V. Khryashchev ◽  
A. A. Lebedev ◽  
A. L. Priorov
Keyword(s):  
Embedded System ◽  
Decision Trees ◽  
Face Detection ◽  
Detection Algorithm ◽  
High Rate ◽  
Battery Life ◽  
Standard Size ◽  
Front Position ◽  
The Embedded System ◽  
The Cost

Face detection algorithm based on a cascade of ensembles of decision trees (CEDT) is presented. The new approach allows detecting faces other than the front position through the use of multiple classifiers. Each classifier is trained for a specific range of angles of the rotation head. The results showed a high rate of productivity for CEDT on images with standard size. The algorithm increases the area under the ROC-curve of 13% compared to a standard Viola-Jones face detection algorithm. Final realization of given algorithm consist of 5 different cascades for frontal/non-frontal faces. One more thing which we take from the simulation results is a low computational complexity of CEDT algorithm in comparison with standard Viola-Jones approach. This could prove important in the embedded system and mobile device industries because it can reduce the cost of hardware and make battery life longer.

scholarly journals PEMANFAATAN RASPBERRY PI SEBAGAI PROCESSOR PADA PENDETEKSIAN DAN PENGENALAN POLA WAJAH

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Fadli Sirait ◽  
Yoserizal Yoserizal
Keyword(s):  
Discriminant Analysis ◽  
Embedded System ◽  
Face Detection ◽  
Principal Component ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Linear Discriminant ◽  
Face Detection And Recognition ◽  
Haar Cascade ◽  
Detection And Recognition

Teknologi biometrik adalah teknologi untuk mengindetifikasi mahluk hidup. Tujuan perancangan  adalah  untuk  membangun  sistem  pendeteksi  wajah  dari  objek  citra  yang  didapat dari  gambar  frame  video  melalui  kamera.  Kemudian  dilakukan  pendeteksi  pola  wajah  yang dikenali  dan  mencari  kemiripan  terhadap  database  model  wajah  menggunakan  Raspberri  Pi berbasis  penggunaan  perangkat  lunak  Free  dan  Opensource.  Perancangan  ini  menggunakan metode  pengenalan  objek  citra  wajah  dengan  Haar  Cascade  Classifier  yang  diimplentasikan pada libarary OpenCV, sedangkan metode pengenalan pola wajah dengan menggunakan analisa PCA (Principal Component Analysist) dan LDA (Linear Discriminant Analysis) menggunakan pemograman  prerangkat  lunak  yang  dibuat  berbasis  Python.  Perangkat  lunak  yang dikembangkan  juga  dijalankan  pada  sistem  operasi  berbasis  Linux  Raspbian  (Jessie  dan Wheezy)  yang  diinstal  di  Raspberry  Pi.  Proses  input  citra  menggunakan  USB  kamera  yang dipasang  pada  Raspberry  Pi  2  Model  B  yang  dilengkapi  dengan  LCD  3,5  inchi.  Berdasarkan data pengujian terhadap 127 input didapat tingkat akurasi untuk pendeteksian satu objek wajah 84-97% sementara performa penggunaan CPU pada Raspberry Pi 41.87-46.25%.Kata kunci: face detection and recognition, Raspberry pi, pengolahan citra, embedded system.

Sign in / Sign up

Export Citation Format

Share Document