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Abandoned Object Detection - 1,062 videos
The dataset consists of 1,062 Full HD videos capturing simulated abandoned object scenarios performed by humans across approximately 60–70 diverse public locations. Videos were captured from a static camera perspective at 1920×1080 resolution and 30 FPS, supporting abandoned object detection, computer vision research, and public safety applications. It is specifically designed to advance detection research in computer vision, providing data for developing and evaluating robust unattended object detection systems.
By utilizing this dataset, researchers and developers can advance their understanding and capabilities in abandoned object detection and recognition technologies. - Get the data
Each video includes detailed annotations and metadata with labels for various actions, enabling researchers to develop machine learning algorithms that can identify abandoned objects such as bags and luggage versus normal daily activity. The dataset features 50 object types, with clothing changed every 10 videos and no more than 3 videos per location, ensuring strong scene and subject variability.
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Researchers can utilize this dataset to explore detection algorithms and recognition techniques that aim to prevent security incidents. The models trained on this data can improve automating abandoned object detection in real-world applications, from airport and railway monitoring to lost and found services and public space surveillance.
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