A study on implementation of real-time intelligent video surveillance system based on embedded module

Conventional surveillance systems for preventing accidents and incidents do not identify 95% thereof after 22 min when one person monitors a plurality of closed circuit televisions (CCTV). To address this issue, while computer-based intelligent video surveillance systems have been studied to notify users of abnormal situations when they happen, it is not commonly used in real environment because of weakness of personal information leaks and high power consumption. To address this issue, intelligent video surveillance systems based on small devices have been studied. This paper suggests implement an intelligent video surveillance system based on embedded modules for intruder detection based on information learning, fire detection based on color and motion information, and loitering and fall detection based on human body motion. Moreover, an algorithm and an embedded module optimization method are applied for real-time processing. The implemented algorithm showed performance of 88.51% for intruder detection, 92.63% for fire detection, 80% for loitering detection and 93.54% for fall detection. The result of comparison before and after optimization about the algorithm processing time showed 50.53% of decrease, implying potential real-time driving of the intelligent image monitoring system based on embedded modules.

In-Flight Tests of Intruder Detection Vision System

In the near future, the integration of manned and unmanned aerial vehicles into the common airspace will proceed. The changes taking place mean that the safety of light aircraft, ultralight aircraft and unmanned air vehicles (UAV) will become an increasing problem. The IDAAS project (Intruder Detection And collision Avoidance System) meets the new challenges as it aims to produce technically advanced detection and collision avoidance systems for light and unmanned aerial vehicles. The work discusses selected elements of research and practical tests of the intruder detection vision system, which is part the of IDAAS project. At the outset, the current formal requirements related to the necessity of installing anticollision systems on aircraft are presented. The concept of the IDAAS system and the structure of algorithms related to image processing are also discussed. The main part of the work presents the methodology developed for the needs of dedicated flight tests, its implementation and the results obtained. The initial tests of the IDAAS system carried out on an ultralight aircraft generally indicate the possibility of the effective detection of intruders in the airspace with the use of vision methods, although they also indicated the existence of conditions in which this detection may prove difficult or even impossible.

Implementation of IoT in Workplace Monitoring and Safety Systems

The continuous and rapid development in facilities in the workplace eventually calls for safety of the workplace premises as well as improved monitoring system. For instance, an intruder alert will be sent even if a client enters the premises. To eradicate this issue, an alert notification has to be sent only when required i.e., during an intruder detection or mishap detection. The data is collected by Raspberry Pi using the sensors interfaced to it. By employing the usage of IoT, data received from the sensors are sent to an IoT platform from where the information is passed as a notification through an email. The detected face from the video recorded by PiCam is sent to a local server using socket programming and the Face recognition is performed in the local server using Haar cascade and LBPH algorithm in Open CV. In case of an intruder detection, an e-mail notification is sent to the user. Similarly, when an accident or disaster is detected such as a fire accident or air pollution, an alert notification is sent to the user through an e-mail.

IoT Based Smart Intruder Detection System For Smart Homes

The intruder detection system is targeted for the private areas, restricted areas and for the domestic home applications to notify the entrance of the intruder or any person to the specified areas. The intruder detection system eliminates the theft and entrance of the persons to the restricted areas by notifying the owner or the gardener through the registered application when the system detects an intruder in the locality. In this work an IoT based intruder detection system named Smart-IDS is proposed to avoid the entrance of the person without any special workforce for the target location. The proposed Smart-IDS detects the intruder using the Node MCU and Ultrasonic sensor and the cloud based Blynk application is employed to send the alert notification to the user. The experiments with the proposed Smart-IDS has performed more efficiently.

Home Automation with Intruder Detection and Energy Control

In previous project we made a home automation system, where we can control our appliances through Blynk app and Google assistant with the help of IFTTT. As an extension we will provide feedback to user whether the appliance is on or off. We will also use sensors like LDR for measuring light intensity in this project to make it smart. We will also include security system where in we have sensors to doors and windows and give buzzer along with an alert message to the user. On a whole we will provide a smart home automation system.