IoT Based intruder Detection Using Real Time Object Detection
Author(s):
THEJAS A V, Dr.Kusumadhara S, Dr.Bhagya H. K
Keywords:
Deep learning, intrusion detection system, IoT, Cloud, transfer learning, smart city, SORT algorithm, YOLO algorithm
Abstract
There is a huge demand Of video surveillance-based intelligent security systems which can automatically detect the unauthorized entry or mal-intentional intrusion into the unattended sensitive areas and notify to the concerned authorities in real time. A novel video-based Intrusion Detection System (IDS) using deep lear ning is proposed with IoT technologies. Here, the You Only Look Once (YOLO) algorithm is used for object detection and intrusion is decided using our proposed algorithm based on the shifted center of mass of the detected object and after detection of intrusion we can categorize the object and send a notification to IoT cloud and notify the concerned user and he can take actions using remote siren systems . Further, Simple Online and Real time Tracking (SORT) algorithm is used for the tracking of the intruder in real-time. The developed system is also implemented and tested for live video stream using NVIDIA Jetson TX2 development platform with an accuracy of 97% and average fps of 30. Here, the proposed IDS is a generic one where the user can select the region of interest (the area to be intrusion free) of any size and shape from the reference (starting) frame and potential intruders such as a person, wild animals, vehicle, etc. from the list of trained object classes. Hence, it can have a wide range of smart city applications such as person intrusion free zone, no vehicle entry zone, no parking zone, smart home security, protect agriculture land From wild Animals etc.
Article Details
Unique Paper ID: 158620

Publication Volume & Issue: Volume 9, Issue 10

Page(s): 177 - 182
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