|Description (include details on usage, files and paper references)||The INRIA People dataset from Navneet Dalal and Bill Triggs [DalalCVPR2005] consists of training and testing data. The training contains 1805 images and X people normalized to 64x128 pixels (see train_64x128_H96). The people are usually standing, but appear in any orientation and against a wide variety of background image including crowds. Many are bystanders taken from the image backgrounds, so there is no particular bias on their pose.
The dataset is typically used for training single-frame pedestrian detectors using additional virtual training samples by flipping, rotating, scaling, shifting and bootstrapping the training data.
[GallCVPR2009] used the provided training data of 614 images with pedestrians and 1218 background images. According to [DalalCVPR2005], the method is used as a classifier on 288 cropped, pre-scaled images with pedestrians and 453 images without them.
|Tags (single words, spaced)||detection, pedestrian, sideview, frontview, human, boundingbox