HOC (Human Orientation Classification) Dataset





HOC dataset is derived by the ETHZ [1] human re-acquisition datasets representing pedestrians in different orientations and (background) conditions, captured by hand-held cameras. ETHZ is structured in three sequences for a total of 8555 images, each image 64x32 pixels containing a pedestrian. We manually split the images into 4 orientation classes (Front, back, Left, and Right), individuating a training and a testing partition. The dataset is challenging because of the low resolution, severe illumination artifacts, occlusions and consistent changes in the scale.

Data

Dataset download: [HOC]

For questions regarding the data please contact diego[dot]tosato[at]univr[dot]it.


Publications

D. Tosato, M. Spera, M. Cristani, and V. Murino, Characterizing humans on
Riemannian manifolds, IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI), Accepted 2012. [pdf]

License Terms

This dataset is made available to the scientific community for non-commercial  research purposes such as academic research, teaching, scientific publications,  or personal experimentation. Permission is granted to use, copy, and distribute the data given that you agree:

1) That the dataset comes "AS IS", without express or implied warranty.  Although every effort has been made to ensure accuracy,  the University of Verona - VIPS lab,  as website host) does not accept any responsibility for errors or omissions.
2) That you include a reference to the above publication in any published  work that makes use of the dataset.
3) That if you have altered the content of the dataset or created derivative work,  prominent notices are made so that any recipients know that they are not receiving  the original data.
4) That you may not use or distribute the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
5) That this original license notice is retained with all copies or derivatives  of the dataset. That all rights not expressly granted to you are reserved by  University of Verona - VIPS lab.

References

[1]  W. R. Schwartz, “Ethz dataset for appearance-based modeling,” http://www.liv.ic.unicamp.br/.wschwartz/datasets.html.