A System for Real-Time Detection of Abandoned Luggage

In this paper, we propose a system for the real-time automatic detection of abandoned luggage in an airport recorded by surveillance cameras. To do this, we use an adapted YOLOv11-s model and a proposed algorithm for detecting unattended luggage. The system uses the OpenCV library for the video proc...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 25; no. 9; p. 2872
Main Authors: Vrsalovic, Ivan, Lerga, Jonatan, Ivasic-Kos, Marina
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 02.05.2025
MDPI
Subjects:
Accuracy
Airports
Algorithms
Analysis
Automation
Cameras
Computer vision
Datasets
deep learning
DETR encoder–decoder transformer
Luggage
object detection
Security systems
Surveillance
YOLOv11
YOLOv8
Germany
Croatia
United States > US
deep learning
DETR encoder–decoder transformer
YOLOv11
OpenCV
YOLOv8
computer vision
object detection
surveillance
luggage detection
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:In this paper, we propose a system for the real-time automatic detection of abandoned luggage in an airport recorded by surveillance cameras. To do this, we use an adapted YOLOv11-s model and a proposed algorithm for detecting unattended luggage. The system uses the OpenCV library for the video processing of the recorded footage, a detector, and an algorithm that analyzes the movement of a person and their luggage and evaluates their spatial and temporal relationships to determine whether the luggage is truly abandoned. We used several popular deep convolutional neural network architectures for object detection, e.g., Yolov8, Yolov11, and DETR encoder–decoder transformer with a ResNet-50 deep convolutional backbone, we fine-tuned them on our dataset, and compared their performance in detecting people and luggage in surveillance scenes recorded by an airport surveillance camera. The fine-tuned model significantly improved the detection of people and luggage captured by the airport surveillance camera in our custom dataset. The fine-tuned YOLOv8 and YOLOv11 models achieved excellent real-time results on a challenging dataset consisting only of small and medium-sized objects. They achieved real-time precision (mAP) of over 88%, while their precision for medium-sized objects was over 96%. However, the YOLOv11-s model achieved the highest precision in detecting small objects, corresponding to 85.8%, which is why we selected it as a component of the abandoned luggage detection system. The abandoned luggage detection algorithm was tested in various scenarios where luggage may be left behind and in situations that may be potentially suspicious and showed promising results.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25092872