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VeRi Dataset

Large-scale benchmark for vehicle Re-Id in the real-world urban surveillance

VeRi Dataset

To facilitate the research of vehicle re-identification (Re-Id), we build a large-scale benchmark dateset for vehicle Re-Id in the real-world urban surveillance scenario, named ā€œVeRiā€. The featured properties of VeRi include: It contains over 50,000 images of 776 vehicles captured by 20 cameras covering an 1.0 km^2 area in 24 hours, which makes the dataset scalable enough for vehicle Re-Id and other related research. The images are captured in a real-world unconstrained surveillance scene and labeled with varied attributes, e.g. BBoxes, types, colors, and brands. So complicated models can be learnt and evaluated for vehicle Re-Id. Each vehicle is captured by 2 āˆ¼ 18 cameras in different viewpoints, illuminations, resolutions, and occlusions, which provides high recurrence rate for vehicle Re-Id in practical surveillance environment. It is also labeled with sufficient license plates and spatiotemporal information, such as the BBoxes of plates, plate strings, the timestamps of vehicles, and the distances between neighbouring cameras.

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VehicleReId
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Task
Image Classification
Annotation Types
Bounding Boxes
50000
Items
12
Classes
50000
Labels
Models using this dataset
Last updated onĀ 
January 20, 2022
Licensed underĀ 
Research Only
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