Design and Implementation of Object Motion Detection Using Telegram

Soni, Gaurav; Saini, Satnam Singh; Malhi, Simarjit Singh; Srao, Bhupinder Kaur; Sharma, Ashim; Puri, Digvijay

doi:10.1109/ictai53825.2021.9673226

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…In this study using the object motion detection feature to detect humans in the building environment [21]. The system will send warning messages and send photos to telegram bots to help detect suspicious object activity to building owners [22]. There are several research gaps in this study, such as the foreign object detection process.…”

Section: Research Methods 21 Related Workmentioning

confidence: 99%

Object Detection and Monitor System for Building Security Based on Internet of Things (IoT) Using Illumination Invariant Face Recognition

Chatisa

Syahbana²,

Wibowo³

2023

KINETIK

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Theft and intrusion are crimes that often occur in neighborhoods when there is opportunity or negligence by owners and security personnel. Many studies have been carried out to improve environmental security by applying cameras as a surveillance medium. However, the camera is not optimal in detecting objects when the lighting conditions are lacking. Therefore, in this study, a monitoring and object detection system was built by applying the Illumination Invariant model. This model is used to improve the appearance of the image from light and shadow reflections. The process of detecting and identifying objects is done by using human facial features (face detection) captured by the camera. The camera used is a Logitec C270 Webcam 720p which is connected via a USB port on the Raspberry Pi 4. The Raspberry Pi 4 processes human face image data and sends the processing results to a MySQL database using the HTTP protocol. Data transmission is done using the Python Flask web framework. The system was successfully run 100% by using black box testing of all functional requirements. Tests on the object detection feature were carried out based on different lighting conditions 15 times by comparing the original image and the results of the Illumination Invariant implementation. Based on the test results obtained object detection accuracy of 86.7%.

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Section: Research Methods 21 Related Workmentioning

confidence: 99%