Market-1501 is a large-scale public benchmark dataset for person re-identification. It contains 1501 identities which are captured by six different cameras, and 32,668 pedestrian image bounding-boxes obtained using the Deformable Part Models pedestrian detector. Each person has 3.6 images on average at each viewpoint. The dataset is split into two parts: 750 identities are utilized for training and the remaining 751 identities are used for testing. In the official testing protocol 3,368 query images are selected as probe set to find the correct match across 19,732 reference gallery images.
812 PAPERS • 9 BENCHMARKS
The CUHK03 consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector. The dataset also provides 20 random train/test splits in which 100 identities are selected for testing and the rest for training
398 PAPERS • 8 BENCHMARKS
MSMT17 is a multi-scene multi-time person re-identification dataset. The dataset consists of 180 hours of videos, captured by 12 outdoor cameras, 3 indoor cameras, and during 12 time slots. The videos cover a long period of time and present complex lighting variations, and it contains a large number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes.
237 PAPERS • 6 BENCHMARKS
Market-1501-C is an evaluation set that consists of algorithmically generated corruptions applied to the Market-1501 test-set. These corruptions consist of Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. Each corruption has five severity levels, resulting in 100 distinct corruptions.
22 PAPERS • 1 BENCHMARK
CUHK03-C is an evaluation set that consists of algorithmically generated corruptions applied to the CUHK03 test-set. These corruptions consist of Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. Each corruption has five severity levels, resulting in 100 distinct corruptions.
9 PAPERS • 1 BENCHMARK
MSMT17-C is an evaluation set that consists of algorithmically generated corruptions applied to the MSMT17 test-set. These corruptions consist of Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. Each corruption has five severity levels, resulting in 100 distinct corruptions.
5 PAPERS • 1 BENCHMARK
The ClonedPerson dataset is a large-scale synthetic person re-identification dataset introduced in the paper "Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification" in CVPR 2022. It is generated by MakeHuman and Unity3D. Characters in this dataset use an automatic approach to directly clone the whole outfits from real-world person images to virtual 3D characters, such that any virtual person thus created will appear very similar to its real-world counterpart. The dataset contains 887,766 synthesized person images of 5,621 identities.
4 PAPERS • 4 BENCHMARKS
RegDB-C is an evaluation set that consists of algorithmically generated corruptions applied to the RegDB test-set (color images). These corruptions consist of Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. Each corruption has five severity levels, resulting in 100 distinct corruptions.
4 PAPERS • 1 BENCHMARK
SYSU-MM01-C is an evaluation set that consists of algorithmically generated corruptions applied to the SYSU-MM01 test-set. These corruptions consist of Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. Each corruption has five severity levels, resulting in 100 distinct corruptions.