图像处理_INRIAPersonDataset(法国国家

1、INRIA Person Dataset(法国国家信息与自动化研究所行人数据库数据摘要：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 HumanDetection and my PhD thesis. The dataset is divided in

2、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

3、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 thebackgrounds of thes

4、e input photos, so ideally there is no particular bias in their pose.Few of images are taken from the web using google images.中文关键词：直立, 行人, 检测, 标注, 规范化,英文关键词：upright,people,detection,annotation,normalized,数据格式：IMAGE数据用途：Research work on detection of upright people in images and video数据详细介绍：INRIA Per

5、son DatasetThis 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

6、with corresponding annotation files, and (b positive images in normalized 64x128 pixel format (as used in the CVPR paper with original negative images.ContributionsThe data set contains images from several different sources:Images from GRAZ 01 dataset, though annotation files are completely new. Ima

7、ges 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, s

8、o ideally there is no particular bias in their pose.Few of images are taken from the web using google images.NoteOnly 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 ins

9、ide the object.Original ImagesFolders '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 'annotation

10、s' (annotation files for positive images in Pascal Challenge format.Normalized ImagesFolders '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 train

11、ing 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

12、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.Negative windowsTo generate negative training windows from no

13、rmalized images, a fixed set of 12180 windows (10 windows per negative image are sampled randomly from 1218 negative training photos providing the initial negative training set. For each detector and parameter combination, a preliminary detector is trained and all negative training images are search

14、ed exhaustively (over a scale-space pyramid for false positives (hard examples'. All examples with score greater than zero are considered hard examples. The method is then re-trained using this augmented set (initial 12180 + hard examples to produce the final detector. The set of hard examples i

15、s subsampled if necessary, so that the descriptors of the final training set fit into 1.7 GB of RAM for SVM training.Starting scale in scale-space pyramid above is one and we keep adding one more level in the pyramid till floor(ImageWidth/Scale>64 and floor(ImageHeight/Scale>128. Scale ratio b

16、etween two consecutive levels in the pyramid is 1.2. Window stride (sampling distance between two consecutive windows at any scale is 8 pixels. If after fitting all windows at a scale level some margin remains at borders, we divide the margin by 2, take its floor and shift the whole window grid. For

17、 example, if image size at current level is (75,130, margin (with stride of 8 and window size of 64,128 left is (3,2. We shift all windows by (floor(MarginX/2, floor(MarginY/2. New image width and height are calculated using the formulas: NewWidth=floor(OrigWidth/Scaleand NewHeight=floor(OrigHeight/

18、Scale. Here scale=1 implies the original image size.Also while testing negative images, to create negative windows, we use the same sampling structure.You may download the whole data set from here (970MB. To avoid duplicating images, 'neg' image folder in 'train_64x128_H96' and 'test_64x12