CUHK Occlusion Dataset (for pedestrian detection)
CUHK Occlusion Dataset (for pedestrian detection)
http://www.ee.cuhk.edu.hk/~xgwang/CUHK_pedestrian.html
http://mmlab.ie.cuhk.edu.hk/datasets/cuhk_occlusion/index.html
1. Download
CUHK occlusion data set is for research on activity analysis and crowded scenes. This dataset contains 1063 images with occluded pedestrians from the datasets of Caltech [1], ETHZ [2], TUD-Brussels [3], INRIA [4], Caviar[5] and images collected by us.
It is divided into 10 clips and can be downloaded from the following links.
Image set00
Image set01
Image set02
Image set03
Image set04
Image set05
Image set06
Image set07
Image set08
http://www.ee.cuhk.edu.hk/~xgwang/video&labels/set00-occ.seq
http://www.ee.cuhk.edu.hk/~xgwang/video&labels/set01-occ.seq
http://www.ee.cuhk.edu.hk/~xgwang/video&labels/set02-occ.seq
http://www.ee.cuhk.edu.hk/~xgwang/video&labels/set03-occ.seq
http://www.ee.cuhk.edu.hk/~xgwang/video&labels/set04-occ.seq
http://www.ee.cuhk.edu.hk/~xgwang/video&labels/set05-occ.seq
http://www.ee.cuhk.edu.hk/~xgwang/video&labels/set06-occ.seq
http://www.ee.cuhk.edu.hk/~xgwang/video&labels/set07-occ.seq
http://www.ee.cuhk.edu.hk/~xgwang/video&labels/set08-occ.seq
In order to evaluate the performance of human detection on this data set, ground truth of pedestrians all images is manually labeled. It can be downloaded below. A readme file provides the instructions of how to use it.
Ground truth of pedestrians
http://www.ee.cuhk.edu.hk/~xgwang/video&labels/labels.zip
In order to open the sequence and label, please directly run the vbbLabeler.m in the
Caltech Toolbox and open the .seq and .vbb above. More tools of Caltech are provided
here.
http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/files/code3.0.0.zip
http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/
2. Reference
Please cite as:
W. Ouyang and X. Wang, " A Discriminative Deep Model for Pedestrian Detection with Occlusion Handling," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
3. Literature
1. P. Doll´ar, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: An evaluation of the state of the art. TPAMI, Accepted, 2011.
2. A. Ess, B. Leibe, and L. V. Gool. Depth and appearance for mobile scene analysis. In ICCV, 2007.
3. C.Wojek, S.Walk, and B. Schiele. Multi-cue onboard pedestrian detection. In CVPR, 2009.
4. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2005.
5. R. Fisher. http://homepages.inf.ed.ac.uk/rbf/CAVIAR/.