This dataset contains 1203 individuals captured from two disjoint camera views. To each person, one to twelve images are captured from one to six different orientations under one camera view and are normalized to 128x64 pixels. This dataset is constructed based on the Market-1501 benchmark data and the orientation label for each image has been manually annotated.
2 PAPERS • NO BENCHMARKS YET
The 42Street dataset is based on a theater play as an example of such an application. The dataset is created using a public recording of the 42Street theatre play [42street]. The play is 1.5 hours long and was split into 5 equally long parts of 20 minutes each, with various clothes changes between the different parts.
1 PAPER • NO BENCHMARKS YET
This dataset contains 971 identities from two disjoint camera views. Each identity has two samples per camera view. It is used for Person Re-identification.
Correlated Corrupted Dataset is an evaluation set that consists of realistic visible-infrared (V-I) corruptions allowing for models' corruption robustness evaluation. Initially proposed for multimodal person re-identification, our dataset can also be used for the evaluation of V-I cross-modal approaches. Corruptions of the visible modality are the twenty corruptions proposed by Chen & al. in the "Benchmarks for Corruption Invariant Person Re-identification" paper. Corruptions of the infrared modalities have been proposed in our paper, introducing 19 corruptions that respect the infrared modality encoding. In practice, for co-located visible-infrared cameras, weather-related corruptions should, for example, affect each camera. Also, blur-related corruption would likely occur in both visible and infrared cameras. This dataset tackles this aspect by considering the eventual correlations that may occur from one modality camera to another.
Collected data from two distinct experiments in immersive, interactive VR where participants performed dynamic tasks as their eye, head, and hand movements were recorded. In the second experiment, a range of real-time privacy mechanisms are applied to eye gaze in real-time.
MVB (Multi View Baggage) is a dataset for baggage ReID task which has some essential differences from person ReID. The features of MVB are three-fold. First, MVB is the first publicly released large-scale dataset that contains 4519 baggage identities and 22660 annotated baggage images as well as its surface material labels. Second, all baggage images are captured by specially-designed multi-view camera system to handle pose variation and occlusion, in order to obtain the 3D information of baggage surface as complete as possible. Third, MVB has remarkable inter-class similarity and intra-class dissimilarity, considering the fact that baggage might have very similar appearance while the data is collected in two real airport environments, where imaging factors varies significantly from each other.
The ULI-RI dataset is generated using the Unreal Engine 4 to simulate various outdoor environments with 115 high-quality 3D human models. For each person identity, we controlled and quantitatively labeled the illumination intensity, view point (model z-rotation angle), and background to create 512 images. There are total 115 x 512 = 58880 images in the ULI-RI dataset.
Uncorrelated Corrupted Dataset is an evaluation set that consists of realistic visible-infrared (V-I) corruptions allowing for models' corruption robustness evaluation. Initially proposed for multimodal person re-identification, our dataset can also be used for the evaluation of V-I cross-modal approaches. Corruptions of the visible modality are the twenty corruptions proposed by Chen & al. in the "Benchmarks for Corruption Invariant Person Re-identification" paper. Corruptions of the infrared modalities have been proposed in our paper, introducing 19 corruptions that respect the infrared modality encoding. In practice, the corruptions are applied randomly and independently to the visible and the infrared cameras, making it more suited to a not co-located camera setting.
The X-MARS dataset proposes new splits for the MARS dataset, to allow for cross-evaluation with the Market-1501 dataset without training and test overlap between the two datasets.