Human and Luggage Analysis for Abandoned Object Detection
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Abstract
This paper presents a method to analyze videos for Abandoned Object Detection (AOD) and ownership identification. Abandoned luggage is a risky event that can actually occur in many public areas such as train stations, airports, or other important places in the building. Deep learning is used for person and luggage detection. The dataset is trained for both people and luggage images including backpacks, handbags and suitcases for more than 12,000 images. In this work, we use the YOLOv3 model that can be processed in real time with 98% accuracy. For ownership identification and abandoned detection, we propose a method that evaluates the spatial relationships and trajectories of each person and luggage. Ownership identification yields 65.1% accuracy while abandoned detection provides 66.6% accuracy.
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