RSTPReid contains 20505 images of 4,101 persons from 15 cameras. Each person has 5 corresponding images taken by different cameras with complex both indoor and outdoor scene transformations and backgrounds in various periods of time, which makes RSTPReid much more challenging and more adaptable to real scenarios. Each image is annotated with 2 textual descriptions. For data division, 3701 (index < 18505), 200 (18505 <= index < 19505) and 200 (index >= 19505) identities are utilized for training, validation and testing, respectively (Marked by item 'split' in the JSON file). Each sentence is no shorter than 23 words.
The ITCPR dataset is a comprehensive collection specifically designed for the Zero-Shot Composed Person Retrieval (ZS-CPR) task. It consists of a total of 2,225 annotated triplets, derived from three distinct datasets: Celeb-reID, PRCC, and LAST.
We introduce Occluded PoseTrack-ReID (or simply Occ-PTrack), a new ReID dataset we built out of the annotation available with PoseTrack21, a popular video benchmark for multi-person pose tracking, that features keypoints and cross-video identity annotations. Unlike previous ReID datasets focused on street surveillance, Occ-PTrack consists of images from everyday life videos, primarily from sports activities. Occ-PTrack is divided into a train/test that includes 1000/1411 identities with 17.898/13.412 images from 474/170 videos, which is roughly equivalent in terms of scale to other popular ReID datasets (e.g. Market-1501, Occluded-Duke, ...). To assess the ReID model’s performance in multi-person occlusion scenarios, we select the most cluttered images of each identity in the test set as query samples, and the remaining test images as gallery samples. Cluttered images corresponds to multi-persons occlusions scenarios where either the front (occluding) or back (occluded) person is the ReID target, to evaluate the model ability to re-identify individuals in both scenarios. We provide further details about our proposed dataset in the supp. materials. Occ-PTrack is challenging as persons within the same video exhibit a high degree of visual resemblance since they often wear similar sports kits.
The TVPReid dataset contains 6559 pedestrian videos, each of which is annotated with two text descriptions, for a total of 13118 descriptions. The sentence descriptions are in a natural language style and contain rich details about the pedestrian's appearance, actions, and environmental elements that the pedestrian interacts with. The average sentence length of the TVPReid dataset is 30 words, and the longest sentence contains 83 words.