ALERT Airport Re-Identification Dataset

As part of the ALERT video analytics effort, researchers at Northeastern University and Rensselaer Polytechnic Institute developed an annotated dataset that accurately reflects the real-world person re-identification problem. The dataset was constructed using video data from the six cameras installed post central security checkpoint at an active commercial airport within the United States.

Each of the videos, were temporally segmented into 40 short clips, each of a duration of about 5 minutes. All the people appearing in each of these short clips were automatically detected and tracked using existing person detection and tracking algorithms. Subsequently, all the detections and tracks were manually annotated.

Some statistics of the ALERT re-identification dataset:

Number of people: 9651
Number of images: 39902
Number of cameras: 6
Average number of images per person: 3.13
Resolution of each image: 128 x 64

An up-to-date benchmark on the dataset is available as part of our systematic study of the existing state of the art in person re-identification, which can be accessed online:

A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets, Srikrishna Karanam, Mengran Gou, Ziyan Wu, Angels Rates-Borras, Octavia Camps, Richard J. Radke, arXiv preprint 1605.09653, last revised Nov. 26, 2016. https://arxiv.org/abs/1605.09653v3

This dataset can be requested by completing the following form and requesting the “Airport Re-Identification Dataset”: ALERT Dataset Request Form

Example of dataset thumbnails

All the people appearing in each of these short clips were automatically detected and tracked using existing person detection and tracking algorithms.

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