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1. 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 "VeRi". 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 ∼ 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.

2. Download

In order to guarantee the quality, the dataset is under revision. To encourage related research, we will provide the dataset according to your request. Please email your full name and affiliation to the contact person (xinchenliu at bupt dot edu dot cn). We ask for your information only to make sure the dataset is used for non-commercial purposes. We will not give it to any third party or publish it publicly anywhere.

3. Citation

If you use the dataset, please kindly cite the following paper:

• 1. Liu X., Liu W., Ma H., Fu H.: Large-scale vehicle re-identification in urban surveillance videos. In: IEEE International Conference on Multimedia and Expo. (2016) accepted.
• 2. Liu X., Liu W., Mei T., Ma H. A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance. In: European Conference on Computer Vision. Springer International Publishing, 2016: 869-884.