Downloads
Person Re-ID 2011 Dataset
This dataset was created in co-operation with the Austrian Institute of Technology for the purpose of testing person re-identification approaches. The dataset consists of images extracted from multiple person trajectories recorded from two different static surveillance cameras. Images from these cameras contain a viewpoint change and a stark difference in illumination, background and camera characteristics. Since images are extracted from trajectories, several different poses per person are available in each camera view. We have recorded 475 person trajectories from one view and 856 from the other one, with 245 persons appearing in both views. Details can be found here.
Download: prid_2011.zip, prid_2011_results.zip
Selected Publications
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Person Re-Identification by Descriptive and Discriminative Classification
In Proc. Scandinavian Conf. on Image Analysis, 2011
(The original publication is available at www.springerlink.com)
Contact: Martin Hirzer
LibBOB
BOB is an C++ online learning toolbox for computer vision that is easy to use, leightweight and simple. It supports several levels for learning and to exchange components of the learner by modifying the configuration file. BOB has been initially started by Martin Godec in mid of 2009 at the Institute for Computer Graphics and Vision (ICG) at Graz University of Technology to replace and merge older Frameworks and Code that was present at the time. The provided pre-compiled linux library supports binary and mutli-class classification and different configuration parameters. It has been compiled under Ubuntu 9.10 (Karmic Koala, 64 bit) using OpenCV 2.0. If you want to get sourcecode for academic or personal useage, please contact us.Download: libbob-1.0
Selected Publications
- Context-driven Clustering by Multi-class Classification in an Active Learning Framework In Proc. Workshop on Use of Context in Video Processing (CVPR), 2010
- Online Multi-Class LPBoost In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2010
- TransientBoost: On-line Boosting with Transient Data In Proc. IEEE Online Learning for Computer Vision Workshop (CVPR), 2010
- On Robustness of On-line Boosting - A Competitive Study In Proc. IEEE On-line Learning for Computer Vision Workshop, 2009
- On-line Random Forests In Proc. IEEE On-line Learning for Computer Vision Workshop, 2009
- Online Random Forests In Proc. IEEE On-line Learning for Computer Vision Workshop, 2009
Contact: Martin Godec
Longterm Pedestrian Dataset
Longterm Dataset (24h / 7 Days / ~1fps)Download: longterm dataset
Selected Publications
- Classifier Grids for Robust Adaptive Object Detection In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2009
Contact: Sabine Sternig
Multi-Camera and Virtual PTZ
The dataset contains the video streams and calibrations of several static cameras and one panoramic video from a spherical camera for two scenarios, both indoor and outdoor.
The panoramic imagery can be used to simulate a PTZ camera with the provided implementation of the virtual PTZ (vPTZ) camera.
Download: Datasets, vPTZ implementation
Selected Publications
- Unsupervised Calibration of Camera Networks and Virtual PTZ Cameras In Proc. Computer Vision Winter Workshop, 2012
Contact: Horst Possegger, Sabine Sternig
Action Recognition
Here we will provide annotated benchmark data sets as well as source code for the application of Action Recognition soon ... For now, have a look at our presentation video for the 17th Computer Vision Winter Workshop below.Demo Real-time Action Recognition
Contact: Thomas Mauthner
Multi-Camera Datasets
- Easy Data Set (just one person)
- Medium Data Set (3-5 persons, used for the experiments)
- Hard Data Set (crowded scene, 5+ persons)
Selected Publications
- Centralized Information Fusion for Learning Object Detectors in Multi-Camera Networks In Proc. Workshop of the Austrian Association for Pattern Recognition, 2010
- Multiple Instance Learning from Multiple Cameras In Proc. IEEE Workshop on Camera Networks (CVPR), 2010
- Online Learning of Person Detectors by Co-Training from Multiple Cameras In Multi-Camera Networks, Principles and Applications, pages 313-334, Academic Press, 2009
- Visual On-line Learning in Distributed Camera Networks In Proc. Int'l Conf. on Distributed Smart Cameras, 2008
Contact: Peter M. Roth
Text and Vision (TVGraz) Dataset
TVGraz is an annotated multi-modal dataset which currently contains 10 visual object categories , 4030 images and associated text. The visual appearance of the objects in the dataset is challenging and offers a less biased benchmark. The objective of the multi-modal dataset is to provide a common means for evaluation of object categorization research based on text and vision.
The archive "TVGraz_script.tar.gz" contain a python script name "download_TVGRAZ_dataset.py", which will download TVGraz dataset images and text from their respective urls, upon execution and according to the "category_list.txt" file. After downloading the textual data will be in raw format per category per image.
Download: TVGraz dataset capturing tool
Contact: Inayatullah Khan
