The “VehicleID” dataset contains CARS captured during the daytime by multiple real-world surveillance cameras distributed in a small city in China. There are 26,267 vehicles (221,763 images in total) in the entire dataset. Each image is attached with an id label corresponding to its identity in real world. In addition, the dataset contains manually labelled 10319 vehicles (90196 images in total) of their vehicle model information(i.e.“MINI-cooper”, “Audi A6L” and “BWM 1 Series”).
93 PAPERS • 3 BENCHMARKS
VeRi-776 is a vehicle re-identification dataset which contains 49,357 images of 776 vehicles from 20 cameras. The dataset is collected in the real traffic scenario, which is close to the setting of CityFlow. The dataset contains bounding boxes, types, colors and brands.
48 PAPERS • 1 BENCHMARK
CityFlow is a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions.
29 PAPERS • 1 BENCHMARK
Veri-Wild is the largest vehicle re-identification dataset (as of CVPR 2019). The dataset is captured from a large CCTV surveillance system consisting of 174 cameras across one month (30× 24h) under unconstrained scenarios. This dataset comprises 416,314 vehicle images of 40,671 identities. Evaluation on this dataset is split across three subsets: small, medium and large; comprising 3000, 5000 and 10,000 identities respectively (in probe and gallery sets).
24 PAPERS • NO BENCHMARKS YET
VehicleX is a large-scale synthetic dataset. Created in Unity, it contains 1,362 vehicles of various 3D models with fully editable attributes.
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VRAI is a large-scale vehicle ReID dataset for UAV-based intelligent applications. The dataset consists of 137, 613 images of 13, 022 vehicle instances. The images of each vehicle instance are captured by cameras of two DJI consumer UAVs at different locations, with a variety of view angles and flight-altitudes (15m to 80m).
4 PAPERS • 2 BENCHMARKS
Vehicle-Rear is a novel dataset for vehicle identification that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates.
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Vehicle-1M involves vehicle images captured across day and night, from head or rear, by multiple surveillance cameras installed in cities. There are totally 936,051 images from 55,527 vehicles and 400 vehicle models in the dataset. Each image is attached with a vehicle ID label denoting its identity in real world as well as a vehicle model label indicating the make, model and year of the vehicle(i.e. "Audi-A6-2013"). All publications using Vehicle-1M dataset should cite the paper below: Haiyun Guo, Chaoyang Zhao, Zhiwei Liu, Jinqiao Wang, Hanqing Lu: Learning coarse-to-fine structured feature embedding for vehicle re-identification. AAAI 2018.
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