RGBD-ID - Pattern Analysis and Computer Vision

[RGBD-ID] - Introduction

RGBD-ID

INTRODUCTION

We present a new dataset for person re-identification using depth information. The main motivation is that the standard techniques (such as SDALF) fail when the individuals change their clothing, therefore they cannot be used for long-term video surveillance. Depth information is the solution to deal with this problem because it stays constant for a longer period of time. While several datasets for appearance-based re-identification exist, the literature still misses a dataset that provides also depth. This dataset aims at promoting the RGB-D re-identification research.

[RGBD-ID] - Details

DETAILS

The proposed dataset is composed by four different groups of data collected using the Kinect. The first group of data has been obtained by recording 79 people with a frontal view, walking slowly, avoiding occlusions and with stretched arms ("Collaborative"). This happened in an indoor scenario, where the people were at least 2 meters away from the camera. The second ("Walking1") and third ("Walking2") groups of data are composed by frontal recordings of the same 79 people walking normally while entering the lab where they normally work. The fourth group ("Backwards") is a back view recording of the people walking away from the lab. Since all the acquisitions have been performed in different days, there is no guarantee that visual aspects like clothing or accessories will be kept constant. Moreover, we asked some people to dress the same t-shirt in "Walking2". This is useful to highlight the power of RGB-D re-identification compared with standard appearance-based methods.

We provide 5 synchronized information for each person: 1) a set of 5 RGB images, 2) the foreground masks, 3) the skeletons, 4) the 3d mesh (ply), 5) the estimated floor. We also provide a MATLAB script to read the data. Since the data are in standard formats (images, text and ply files) you can easily implement your own parser using your favourite programming language.

Instrunctions are in README.txt.

SAMPLE IMAGES

[RGBD-ID] - Request

How to get the dataset

To obtain this dataset, we ask you to complete, sign and return the form below. After that, I will send you the credentials to download it. Note that the dataset is available only for research purposes.

Fill out this form: Request Form
Send it to: Pavistech, indicating as subject [RGBD-ID Dataset] (Note: you should send the email from an email address that is linked to your research institution/university)
Wait for the credentials
Download the dataset and the ground truth (link)

If you have any questions, contact the first author of the paper Igor Barbosa: igorbb[at]gmail[dot]com.

[RGBD-ID] - Citation

How to cite our work

@inproceedings{Barbosa:reid12,
  author = {Barbosa, B. I. and Cristani, M. and Del Bue, A. and Bazzani, L. and Murino, V.},
  title = {Re-identification with RGB-D sensors},
  booktitle = {First International Workshop on Re-Identification},
  year = {2012},
  month = {October}
}

[RGBD-ID] - References

REFERENCES

[1] B. I. Barbosa, M. Cristani, A. Del Bue, L. Bazzani, and V. Murino, Re-identification with RGB-D sensors, First International Workshop on Re-Identification (RE-ID), 2012.

[RGBD-ID] - Links

Download

Some opensource software for RGB-D cameras you may find interesting

Recorder Player Acknowledgements to Igor B. Barbosa

[RGBD-ID] - Projects

Projects

Person Re-Identification

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