Secure Vehicle With Driver Assistant System using Raspberry Pi and OpenCV

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Description

Secure Vehicle With Driver Assistant System using Raspberry Pi and OpenCV

OBJECTIVE:

  • The main aim of this project is to secure the vehicle by using computer vision
  • Secondly, this project aims to provide a driver assistance system for safe and secure driving. 

Secure Vehicle With Driver Assistant System using Raspberry Pi and OpenCV

ABSTRACT:

Nowadays the cases of theft are increasing very much, especially in developing countries like India. Security for vehicles is available in more areas. Nowadays GPS and buzzer modules are used to only identify theft. In this project, we use a Face recognition sensor, and mobile applications are used for detection and tracking. We use a python application to recognize the face and compare the face to the owner.  If both faces are the same vehicle gets unlocked otherwise the concerned person’s face image will be captured and sent to the owner through email owner will get an SMS alert to check the mail. If any person tries to unlock the car or damages the device, it will send messages to the responsible person. These kinds of projects are used to reduce vehicle theft. In all areas, IoT or GSM services cannot be used because of the lack of mobile networks. In this case, LoRa communication is used to alert the owner. 

Apart from being theft-proof, the car is equipped with driver assistance and a safety system that consists of alcohol detection and seatbelt detection. If either of the sensors gives a negative reading vehicle will not turn on. Driver drowsiness level is also detected through a camera on the dashboard. Secure Vehicle With Driver Assistant System using Raspberry Pi and OpenCV


EXISTING SYSTEM:

  • Sensor-based System
  • Sound-based System

PROPOSED SYSTEM:

  • Image-Based Identification
  • Face recognition
  • Classification
  • Sending alert message
  • Internet SMS is used for theft alerts.
  • Alcohol and sleep detection with seatbelt detection

EXISTING SYSTEM:

Sensor-based System: It detects and responds to input based on the physical environment. The input could be light, heat, motion, etc…

DISADVANTAGES: 

  1. Sensitive to temperature
  2. Limited lifetime

Sound-based System: This system gives an alert to the driver by sounding an alarm 


DISADVANTAGES:

  1. Local alert only
  2. Not possible to alert the driver in a remote location 

 ADVANTAGES:

  • Cost-Effective
  • Accurate Identification

BLOCK DIAGRAM:

Secure Vehicle With Driver Assistant System using Raspberry Pi and OpenCV
Secure Vehicle With Driver Assistant System using Raspberry Pi and OpenCV

Driver node(Rx):

Secure Vehicle With Driver Assistant System using Raspberry Pi and OpenCV 2


HARDWARE:

  • Raspberry pi
  • Arduino Uno
  • MQ3
  • Seatbelt sensor
  • Lora 
  • Buzzer

SOFTWARE:

  • OPERATING SYSTEM: Raspbian OS
  • PROGRAMMING PLATFORM: PYTHON IDLE, Arduino IDE
  • IMAGE PROCESSING LIBRARY: OPEN CV
  • MATRIX CONVERSION LIBRARY: NUMPY

APPLICATION:

  • Surveillance application
  • ADAS

Secure Vehicle With Driver Assistant System using Raspberry Pi and OpenCV

REFERENCES:

  • [1] Y.Chang.,Chen, H.,Chiang, F., H. Wang,(2010). Toward Real-Time Precise Point Positioning: Differential GPS Based on IGS Ultra Rapid Product, SICE Annual Conference, The Grand Hotel, Taipei, Taiwan August 18-21 
  • [2] Asaad.M.J.Al-Hindawi, Ibraheem Talib, “Experimentally Evaluation of GPS/GSM Based System Design”, Journal of Electronic Systems Volume 2 Number 2 June 2012. 
  • [3] Mandeep Singh, Kunal Maurya, Neelu Jain, “Real-Time Vehicle Tracking System using GSM  and GPS  Technology An  Anti-theft Tracking System,” International Journal of Electronics and Computer Science Engineering.ISSN 22771956/V1N3-1103-1107. 
  • [4] Viswaprakash Babu & Vikram Kulkarni, “embedded smart car security system on face detection’, special issue of IJCCT, ISSN(Online):2231-0371, ISSN(Print): 09757449, volume-3, issue-1. 
  • [5] K.Karthick, V.Ramya, B.Palaniappan, “Embedded Controller for Vehicle In-Front Obstacle Detection and cabin Safety Alert System”, International Journal of Computer Science & Information Technology (IJCSIT) Vol 4, No 2, April 2012 
  • [6] Kai-Tai Song, Chih-Chieh Yang, of National Chiao Tung University, Taiwan, “Front Vehicle Tracking Using Scene Analysis”, Proceedings of the IEEE International Conference on Mechatronics & Automation 2005.

 

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