# Human Attribute Recognition--
## 4 Datasets
### FAR Datasets  
* Parse27k dataset. Pedestrian attribute recognition in sequences (Parse27k) dataset [161] contains over 27,000 pedestrian images, annotated with 10 attributes. The images were captured by a moving camera across a city environment; every 15 th video frame was fed to the Deformable Part Model(DPM) pedestrian detector [78] and the resulting bounding boxes were annotated with the 10 attributes based on binary or multinomial propositions. As opposed to other datasets, the authors also included an N/A state (i.e., the labeler cannot decide on that attribute). The attributes from this dataset include gender information (3 categories--_male, female, N/A_), accessories (_Bag on Left Shoulder, Bag on Right Shoulder Bag in Left Hand, Bag in Right Hand, Backpack_; each with three possible states: _yes, no, N/A_), orientation (with 4 + N/A or 8 + N/A_ discretizations) and action attributes: _posture_ (_standing, walking, sitting_ and _N/A_) and _isPushing_ (_yes, no, N/A_). As the images were initially processed by a pedestrian detector, the images of this dataset consist of a fixed-size bounding region of interest, and thus are strongly aligned and contain only a subset of possible human poses. * CRP dataset. CRP (**Caltech Roadside Pedestrians**) [162] dataset was captured in real world conditions, from a moving vehicle. The position (bounding-box) of each pedestrian, together with 14 body joints are annotated in each video frame. CRP comprises 4222 video tracks, with 27,454 pedestrian bounding boxes. The following attributes are annotated for each pedestrian--age (5 categories: _child, teen, young adult, middle aged_ and _senior_), gender (2 categories--_female_ and ), weight (3 categories: _Under, Healthy_ and _Over_), and clothing style (4 categories--_casual_, _light athletic_, _workout_ and _dressy_). The original, un-cropped videos together with the annotations are publicly available. * Describing People dataset. Describing People dataset [68] comprises 8035 images from the H3D [163] and the PASCAL VOC 2010 [164] datasets. The images from this database are aligned, in the sense that for each person, the image is cropped (by leaving some margin) and then scaled so that the distance between the hips and the shoulders is 200 pixels. The dataset features 9 binary (True/False) attributes, as follows: gender (_is male_), appearance (_long hair_), accessories (_glasses_)and several clothing attributes (_has hat, has t-shirt, has shorts, has jeans, long sleeves, long pants_). The dataset was annotated on Amazon Mechanical Turk by five independent labelers; the authors considered a valid label if at least four of the five annotators agreed on its value. * HAT dataset. **H**uman **AT**tributes (HAT) [66; 78] contains 9344 images gathered from Flickr; for this purpose, the authors used more than 320 manually specified queries to retrieve images related to people and then, employed an off-the-shelf person detector to crop the humans in the images. The false positives were manually removed. Next, the images were labeled with 27 binary attributes; these attributes incorporate information about the gender (Female), age (Small baby, Small kid, Teen aged, Young (college), Middle Aged, Elderly), clothing (Wearing tank top, Wearing tee shirt, Wearing casual jacket, Formal men suit, Female long skirt, Female short skirt, Wearing short shorts, Low cut top, Female in swim suit, Female wedding dress, Bermuda/beach shorts), pose (Frontal pose, Side pose, Turned Back), Action (Standing Straight, Sitting, Running/Walking, Crouching/bent, Arms bent/crossed) and occlusions (Upper body). The images have high variations both in image size and in the subject's position. * WIDER dataset. WIDER Attribute dataset [75] comprises a subset of 13,789 images selected from the WIDER database [165], by discarding the images full of non-human objects and the images in which the human attributes are indistinguishable; the human bounding boxes from these images are annotated with 14 attributes.