Real-time Vehicle monitoring for traffic surveillance and adaptive change detection using Raspberry Pi camera module

2017 ◽  
Author(s):  
Tasnim Sorwar ◽  
Sabbir Bin Azad ◽  
Sayed Rizban Hussain ◽  
Azfar Isa Mahmood
Keyword(s):  
Change Detection ◽  
Real Time ◽  
Adaptive Change ◽  
Raspberry Pi ◽  
Traffic Surveillance ◽  
Vehicle Monitoring ◽  
Camera Module

scholarly journals Traffic Surveillance Using Smart Drone

2020 ◽  
pp. 356-363
Author(s):  
Mounica B ◽  
Sathya N ◽  
Likitha R ◽  
Meghana C A
Keyword(s):  
Real Time ◽  
Raspberry Pi ◽  
Traffic Surveillance ◽  
The Road ◽  
Vehicle Monitoring ◽  
The Government ◽  
Day By Day ◽  
Traffic Rules

Day by day the number of vehicles is increasing very fast as the demand is increasing. So, the details of the vehicles are very important to maintain for the government of a country. Information like ownership, insurance, emission, road tax etc., need to be maintained and accessed very efficiently and easily. Even for crime purpose the vehicles are used. So, depending on the demand of the requirements we have proposed this model for real time vehicle monitoring and intimating for violation of traffic rules using drone. In our proposed system, the drone is fitted with cameras and Raspberry Pi. The drone will keep on monitoring the non-parking areas from above the level of ground. The drone will capture the image and detect the vehicle and if the vehicle is not moving after two minutes, transmit the vehicle image to the server along with the road signal code.

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

Walking Motion Real-time Detection Based on Walking Stick Equipped with MPU and Raspberry Pi

2019 ◽  
Author(s):  
Junyi Wang ◽  
Mohamad Saada ◽  
Haibin Cai ◽  
Qinggang Meng
Keyword(s):  
Real Time ◽  
Raspberry Pi ◽  
Walking Motion ◽  
Real Time Detection ◽  
Walking Stick

scholarly journals Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Inventions ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 42
Author(s):  
Worasit Sangjan ◽  
Arron H. Carter ◽  
Michael O. Pumphrey ◽  
Vadim Jitkov ◽  
Sindhuja Sankaran
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Crop Growth ◽  
Sensor System ◽  
Plant Phenotyping ◽  
Raspberry Pi ◽  
The Internet ◽  
Growth Stages ◽  
Crop Breeding ◽  
Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

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