# Human Attribute Recognition--
## 4 Datasets
### PAR Datasets  
* PETA dataset. **PE**des**Trian **Attribute** (PETA) [152] dataset combines 19,000 pedestrian images gathered from 10 publicly available datasets; therefore the images present large variations in terms of scene, lighting conditions and image resolution. The resolution of the images varies from 17 \(\times\) 39 to 169 \(\times\) 365 pixels. The dataset provides rich annotations: the images are manually labeled with 61 binary and 4 multi-class attributes. The binary attributes include information about demographics (gender: _Male_, age: _Age16-30_, _Age31-45,Age46-60_, _AgeAbove61_), appearance (_long hair_), clothing (_T-shirt, Trousers_ etc.) and accessories (_Sunglasses, Hat, Backpack_ etc.). The multi-class attributes are related to (eleven basic) color(s) for the upper-body and lower-body clothing, shoe-wear, and hair of the subject. When gathering the dataset, the authors tried to balance the binary attributes; in their convention, a binary class is considered balanced if the maximal and minimal class ratio is less than 20:1. In the final version of the dataset, more than half of the binary attributes (31 attributes) have a balanced distribution. * RAP dataset. Currently, there are two versions of the RAP (**R**ichly **A**nnotated **P**edestrian) dataset. The first version, RAP-v1 v1 [153] was collected from a surveillance camera in shopping malls over a period of three months; next, 17 hours of video footage were manually selected for attribute annotation. In total, the dataset comprises 41,585 annotated human silhouettes. The 72 attributes labeled in this dataset include demographic information (_gender_ and _age_), accessories (_backpack_, _single shoulder bag_, _handbag_, _plastic bag_, _paper bag_ etc. ), human appearance (_hair style_, _hair color_, _body shape_) and clothing information (_clothes style_, _clothes color_, _footware style_, _footware color_ etc.). In addition, the dataset provides annotations about occlusions, viewpoints and body-parts information. The second version of the RAP dataset [108] is intended as a unifying benchmark for both person retrieval and person attribute recognition in real-world surveillance scenarios. The dataset was captured indoor, in a shopping mall and contains 84,928 images (2589 person identities) from 25 different scenes. High-resolution cameras (1280 \(\times\) 720) were used to gather the dataset, and the resolution of human silhouettes varies from 33 \(\times\) 81 to 415 \(\times\) 583 pixels. The attributes annotated are the same as in RAP v2 (72 attributes, and occlusion, viewpoint, and body-parts information). * DukeMTMC dataset. **Duke**MTMC-reid (**M**ulti-**T**arget, **M**ulti-**C**amera) dataset [154] was collected in Duke's university campus and contains more than 14 h of video sequences gathered from 8 cameras, positioned such that they capture crowded scenes. The main purpose of this dataset was person re-identification and multi-camera tracking; however, a subset of this dataset was annotated with human attributes. The annotations were provided at the identity level, and they included 23 attributes, regarding the gender (male, female), accessories: wearing hat (yes, no), carrying a backpack (yes, no), carrying a handbag (yes, no), carrying other types of the bag (yes, no), and clothing style: shoe type (boost, other shoes), the color of shoes (dark, bright), length of upper-body clothing (long, short), 8 colors of upper-body clothing (black, white, red, purple, gray, blue, green, brown) and 7 colors of lower-body clothing (black, white, red, gray, blue, green, brown). Due to violation of civil and human rights, as well as privacy issues, since June 2019, Duke University has terminated the DukeMTMC dataset page. * PA-100K dataset. PA-100k dataset [88] was developed with the intention to surpass the existing HAR datasets both in quantity and in diversity; the dataset contains more than 100,000 images captured in 598 different scenarios.