Person Search

The CUKL-SYSY dataset is a large scale benchmark for person search, containing 18,184 images and 8,432 identities. Different from previous re-id benchmarks, matching query persons with manually cropped pedestrians, this dataset is much closer to real application scenarios by searching person from whole images in the gallery. Source: http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html Image Source: http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html

PRW is a large-scale dataset for end-to-end pedestrian detection and person recognition in raw video frames. PRW is introduced to evaluate Person Re-identification in the Wild, using videos acquired through six synchronized cameras. It contains 932 identities and 11,816 frames in which pedestrians are annotated with their bounding box positions and identities.

MovieNet is a holistic dataset for movie understanding. MovieNet contains 1,100 movies with a large amount of multi-modal data, e.g. trailers, photos, plot descriptions, etc.. Besides, different aspects of manual annotations are provided in MovieNet, including 1.1M characters with bounding boxes and identities, 42K scene boundaries, 2.5K aligned description sentences, 65K tags of place and action, and 92 K tags of cinematic style.

One large-scale database for Text-to-Image Person Re-identification, i.e., Text-based Person Retrieval. Compared with existing databases, ICFG-PEDES has three key advantages. First, its textual descriptions are identity-centric and fine-grained. Second, the images included in ICFG-PEDES are more challenging, containing more appearance variability due to the presence of complex backgrounds and variable illumination. Third, the scale of ICFG-PEDES is larger.

The Market1501-Attributes dataset is built from the Market1501 dataset. Market1501 Attribute is an augmentation of this dataset with 28 hand annotated attributes, such as gender, age, sleeve length, flags for items carried as well as upper clothes colors and lower clothes colors. Source: Color inference from semantic labeling for person search in videos Image Source: https://github.com/vana77/Market-1501_Attribute

Provides consistent ID annotations across multiple days, making it suitable for the extremely challenging problem of person search, i.e., where no clothing information can be reliably used. Apart this feature, the P-DESTRE annotations enable the research on UAV-based pedestrian detection, tracking, re-identification and soft biometric solutions. Source: The P-DESTRE: A Fully Annotated Dataset for Pedestrian Detection, Tracking, Re-Identification and Search from Aerial Devices

The images in DukeMTMC-attribute dataset comes from Duke University. There are 1812 identities and 34183 annotated bounding boxes in the DukeMTMC-attribute dataset. This dataset contains 702 identities for training and 1110 identities for testing, corresponding to 16522 and 17661 images respectively. The attributes are annotated in the identity level, every image in this dataset is annotated with 23 attributes. NOTE: This dataset has been retracted. Source: Pedestrian Attribute Recognition: A Survey Image Source: http://irip.buaa.edu.cn/mars_duke_attributes/index.html

CUHK-QA is a dataset for natural language-based person search using iterative questioning. The dataset consists of 400 images of 360 people, and 20 participants answered 5 specific questions about the appearance of each person. So, each person has labelled 20 images. Average length of combined description for each image is 39.15. All the images have been taken from the test set of CUHK-PEDES dataset. Source: Interactive Natural Language-based Person Search

A balanced dataset of color names and RGB values for training classifiers. Source: https://github.com/Smoltbob/XKCDColors-Dataset