This dataset was collected as part of research work on detection of upright people in images and video. The research is described in detail in CVPR 2005 paper Histograms of Oriented Gradients for Human Detection and my PhD thesis. The dataset is divided in two formats: (a) original images with corresponding annotation files, and (b) positive images in normalized 64x128 pixel format (as used in the CVPR paper) with original negative images.
The data set contains images from several different sources:
Images from GRAZ 01 dataset, though annotation files are completely new.
Images from personal digital image collections taken over a long time period. Usually the original positive images were of very high resolution (approx. 2592x1944 pixels), so we have cropped these images to highlight persons. Many people are bystanders taken from the backgrounds of these input photos, so ideally there is no particular bias in their pose.
Few of images are taken from the web using google images.
only upright persons (with person height > 100) are marked in each image.
Annotations may not be right; in particular at times portions of annotated bounding boxes may be outside or inside the object.
Folders 'Train' and 'Test' correspond, respectively, to original training and test images. Both folders have three sub folders: (a) 'pos' (positive training or test images), (b) 'neg' (negative training or test images), and (c) 'annotations' (annotation files for positive images in Pascal Challenge format).
Folders 'train_64x128_H96' and 'test_64x128_H96' correspond to normalized dataset as used in above referenced paper. Both folders have two sub folders: (a) 'pos' (normalized positive training or test images centered on the person with their left-right reflections), (b) 'neg' (containing original negative training or test images). Note images in folder 'train/pos' are of 96x160 pixels (a margin of 16 pixels around each side), and images in folder 'test/pos' are of 70x134 pixels (a margin of 3 pixels around each side). This has been done to avoid boundary conditions (thus to avoid any particular bias in the classifier). In both folders, use the centered 64x128 pixels window for original detection task.
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