Loitering Detection System using Artificial Intelligence
Loitering detection in public spaces is an important challenge for security and safety. Loitering refers to lingering or waiting in a particular area for an extended period of time without any clear purpose. While occasional brief stops may be harmless, prolonged loitering in restricted or sensitive areas can be a public nuisance or security risk. Manual loitering monitoring through human guards is expensive, inconsistent and not scalable. Hence automated video analytics methods are desirable.
If user select from above two options then it it will open new frame in which user have to select browse video or camera. If user selected browse option then we have to gave input video and model will loitering activity, if loitering activity detected then it sent alert message on registered mail id. User Interface is kept simple and user friendly. This project can be used for university projects for more details contact us from below number.
Loitering detection in a video surveillance system
Loitering in restricted areas is a security concern for many public and private institutions. In this paper, we present an automated loitering detection system using the state-of-the-art YOLOv5 object detector. YOLOv5 is trained on a customized dataset of people and objects of interest to monitor. The trained model is then utilized to analyze real-time video streams from surveillance cameras. We track detected objects over time and implement additional logic to identify loitering behavior – defined as remaining within a fixed radius for longer than a set time threshold. In our implementation, we use a radius of 10 feet and duration threshold of 30 seconds. Object locations are tracked even through short-term detection failures using Kalman Filtering. Our proposed pipeline achieves strong loitering detection performance, quantified using standard metrics like precision, recall and F1 scores. We demonstrate the effectiveness of YOLOv5 for this application and the ability to customize loitering definition by tuning key parameters like duration threshold and radius. The system provides a cost-effective automated solution to enhance security and safety.
In this work, we develop an automated loitering detection system using state-of-the-art object detection algorithms. Object detection has made rapid progress in recent years thanks to deep convolutional neural networks. Among the most popular object detectors is the YOLO (You Only Look Once) family, known for its fast inference speed and high accuracy. We employ the latest iteration YOLOv5, fine-tuned on a customized dataset of people and objects of interest for monitoring loitering.
Project Working of Loitering Detection using YOLOv5 Algorithm:-
Loitering Detection System project developed using python programming language and it is software based and user interface created using tkinter. When user run the project then three options shows.
i). YOLO
ii). YOLO Tiny
iii). Exit Button
Loitering Detection System Project in Python
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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