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[Dataset] MANGO-I

[Dataset] This grocery food dataset contains 1719 videos with 23 classes, which are subdivided into 98 subclasses. The videos were recorded in two SPAR grocery stores in HD (1920x1080 and 1280x720) using five mobile phones (Samsung Galaxy S2, Samsung Galaxy S3, Motorola Moto G, HTC One, LG Nexus 4).

Detailed information and download links.

Selected Publications

  1. MANGO - Mobile Augmented Reality with Functional Eating Guidance and Food Awareness (bib)Georg Waltner, Michael Schwarz, Stefan Ladstätter, Anna Weber, Patrick Luley, Horst Bischof, Meinrad Lindschinger, Irene Schmid, and Lucas Paletta In Proc. International Workshop on Multimedia Assisted Dietary Management (MADIMA, in conjunction with ICIAP), 2015
Contact: Georg Waltner


[Dataset] Volleyball Activity Dataset 2014

[Dataset] This dataset contains 7 challenging volleyball activity classes annotated in 6 videos from professionals in the Austrian Volley League (season 2011/12). A total of 36178 annotations within 18960 frames are provided along with the HD video files (1920x1080 @25fps, DX50 codec). The seven classes consist of 5 volleyball specific classes ('Serve', 'Reception', 'Setting', 'Attack', 'Block') and 2 more general classes ('Stand', 'Defense/Move').

Download: Code (vb14_code.zip (2.5MB)), Videos (graz-arbesbach_2.avi (2GB), graz-arbesbach_3.avi (2GB), graz-arbesbach_4.avi (2GB), graz-arbesbach_5.avi (2GB), graz-gleisdorf_1.avi (3GB), graz-klagenfurt1_2.avi (2GB))

Selected Publications

  1. Improved Sport Activity Recognition using Spatio-temporal Context (bib)Georg Waltner, Thomas Mauthner, and Horst Bischof In Proc. DVS-Conference on Computer Science in Sport (DVS/GSSS), 2014
  2. Indoor Activity Detection and Recognition for Automated Sport Games Analysis (bib)Georg Waltner, Thomas Mauthner, and Horst Bischof In Proc. Workshop of the Austrian Association for Pattern Recognition (AAPR/OAGM), 2014
Contact: Georg Waltner


[Dataset] Annotated Facial Landmarks in the Wild

[Dataset] Annotated Facial Landmarks in the Wild (AFLW) provides a large-scale collection of annotated face images gathered from the web, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total about 25k faces are annotated with up to 21 landmarks per image.

More information

Selected Publications

  1. Robust Face Detection by Simple Means (bib)Martin Koestinger, Paul Wohlhart, Peter M. Roth, and Horst Bischof In Computer Vision in Applications Workshop (DAGM), 2012
  2. Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization (bib)Martin Koestinger, Paul Wohlhart, Peter M. Roth, and Horst Bischof In First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies, 2011
Contact: Martin Köstinger


[Code] Large Scale Metric Learning from Equivalence Constraints

We provide the code and data to reproduce all experiments of our CVPR'12 paper Large Scale Metric Learning from Equivalence Constraints. In this paper, we raise important issues on scalability and the required degree of supervision of existing Mahalanobis metric learning methods. Often rather tedious optimization procedures are applied that become computationally intractable on a large scale. With our Keep It Simple and Straightforward MEtric (KISSME) we introduce a simple though effective strategy to learn a distance metric from equivalence constraints. Our method is orders of magnitudes faster than comparable methods.

More information

Selected Publications

  1. Large Scale Metric Learning from Equivalence Constraints (bib)Martin Koestinger, Martin Hirzer, Paul Wohlhart, Peter M. Roth, and Horst Bischof In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012
  2. Synergy-based Learning of Facial Identity (bib)Martin Koestinger, Peter M. Roth, and Horst Bischof In Proc. DAGM Symposium, 2012
    (Winner of the Best Paper Award)
Contact: Martin Köstinger




[Code] Distractor-Aware Tracker (Generic Object Tracking)


We provide a reimplementation of our CVPR'15 paper In Defense of Color-based Model-free Tracking. In this work, we address color-based online object tracking where neither class-specific prior knowledge nor pre-learned object models are available. Combining a discriminative object-vs-background model with an additional distractor-aware term, we show that tracker based on standard color representations (such as histograms) can achieve state-of-the-art performance on a variety of test sequences.

More information

Download: Code & demo data (~ 5.4 MB, MATLAB)

Selected Publications

  1. In Defense of Color-based Model-free Tracking (bib) (code) Horst Possegger, Thomas Mauthner, and Horst Bischof In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015

Contact: Horst Possegger, Thomas Mauthner




[Code] Occlusion Geodesics (Multi-Object Tracking)


We provide the code of our CVPR'14 paper Occlusion Geodesics for Online Multi-Object Tracking. In this work, we address the problem of correctly assigning noisy detection results to trajectories of multiple objects. In particular, we exploit the spatio-temporal evolution of occlusion regions, detector reliability, and target motion prediction to robustly handle missed detections. In combination with a conservative association scheme for visible objects, this allows for real-time tracking of multiple objects from a single static camera.

More information

Download: Code & demo data (~ 31 MB, MATLAB)

Selected Publications

  1. Occlusion Geodesics for Online Multi-Object Tracking (bib) (code) Horst Possegger, Thomas Mauthner, Peter M. Roth, and Horst Bischof In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014

Contact: Horst Possegger, Thomas Mauthner




[Dataset] ICG Lab 6 (Multi-Camera Multi-Object Tracking)


This dataset contains 6 indoor people tracking scenarios recorded at our laboratory using 4 static Axis P1347 cameras:

For each scenario, we provide the synchronized video streams, the full (extrinsic & intrinsic) camera calibration, manually annotated groundtruth for every 10th frame, as well as a top-view model of the ground-plane.

More information

Download: Datasets (~ 614 MB), evaluation code & protocol (~ 32 KB, MATLAB)

Selected Publications

  1. Robust Real-Time Tracking of Multiple Objects by Volumetric Mass Densities (bib) (data) Horst Possegger, Sabine Sternig, Thomas Mauthner, Peter M. Roth, and Horst Bischof In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013

Contact: Horst Possegger




[Dataset+Code] Multi-Camera and Virtual PTZ


This dataset contains the video streams and calibrations of several static Axis P1347 cameras and one panoramic video from a spherical Point Grey Ladybug3 camera for two scenarios. The first scenario (outdoor) shows a crowded campus of our university, while the second sequence (indoor) was recorded during the preparations of a European handball training game at a sports hall in Graz. The panoramic imagery can be used to simulate a PTZ camera with the provided implementation of the virtual PTZ (vPTZ) camera.

More information

Download: Datasets (~ 440 MB), vPTZ implementation (~ 15 KB, C++)


Selected Publications


Contact: Horst Possegger, Sabine Sternig




[Dataset] PRID 2011

Camera setup

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

  1. Person Re-Identification by Descriptive and Discriminative Classification (bib)Martin Hirzer, Csaba Beleznai, Peter M. Roth, and Horst Bischof In Proc. Scandinavian Conference on Image Analysis (SCIA), 2011
    (The original publication is available at www.springerlink.com)

Contact: Martin Hirzer




[Dataset] PRID 450S

PRID 450S examples

This dataset was created in co-operation with the Austrian Institute of Technology for the purpose of testing person re-identification approaches. It is based on the PRID 2011 dataset and contains 450 image pairs recorded from two different, static surveillance cameras. Additionally, the dataset also provides an automatically generated, motion based foreground/background segmentation as well as a manual segmentation of parts of a person.

Download: prid_450s.zip

Selected Publications

  1. Mahalanobis Distance Learning for Person Re-Identification (bib)Peter M. Roth, Martin Hirzer, Martin Koestinger, Csaba Beleznai, and Horst Bischof In Person Re-Identification, pages 247-267, Springer, 2014
    (The original publication is available at www.springer.com)

Contact: Martin Hirzer




[Code] 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.

Library: libbob-1.0
Sources: libbob-1.0-dev

Selected Publications

  1. Context-driven Clustering by Multi-class Classification in an Active Learning Framework (bib)Martin Godec, Sabine Sternig, Peter M. Roth, and Horst Bischof In Proc. Workshop on Use of Context in Video Processing (CVPR), 2010
  2. Online Multi-Class LPBoost (bib)Amir Saffari, Martin Godec, Thomas Pock, Christian Leistner, and Horst Bischof In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2010
  3. TransientBoost: On-line Boosting with Transient Data (bib)Sabine Sternig, Martin Godec, Peter M. Roth, and Horst Bischof In Proc. IEEE Online Learning for Computer Vision Workshop (CVPR), 2010
  4. On Robustness of On-line Boosting - A Competitive Study (bib)Christian Leistner, Amir R. Saffari A. A., Peter M. Roth, and Horst Bischof In Proc. IEEE On-line Learning for Computer Vision Workshop, 2009
  5. On-line Random Forests (bib)Amir Saffari, Christian Leistner, Jakob Santner, Martin Godec, and Horst Bischof In Proc. IEEE On-line Learning for Computer Vision Workshop, 2009
  6. Online Random Forests (bib)Amir R. Saffari A. A., Christian Leistner, and Horst Bischof In Proc. IEEE On-line Learning for Computer Vision Workshop, 2009

Contact: Martin Godec




[Dataset] Longterm Pedestrian Dataset

Longterm Dataset (24h / 7 Days / ~1fps)

Download: longterm dataset

Selected Publications

  1. Classifier Grids for Robust Adaptive Object Detection (bib)Peter M. Roth, Sabine Sternig, Helmut Grabner, and Horst Bischof In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2009

Contact: Sabine Sternig




[Dataset] 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


[Dataset] Multi-Camera Datasets

Selected Publications

  1. Centralized Information Fusion for Learning Object Detectors in Multi-Camera Networks (bib)Armin Berger, Peter M. Roth, Christian Leistner, and Horst Bischof In Proc. Workshop of the Austrian Association for Pattern Recognition, 2010
  2. Multiple Instance Learning from Multiple Cameras (bib)Peter M. Roth, Christian Leistner, Armin Berger, and Horst Bischof In Proc. IEEE Workshop on Camera Networks (CVPR), 2010
  3. Online Learning of Person Detectors by Co-Training from Multiple Cameras (bib)Peter M. Roth, Christian Leistner, Helmut Grabner, and Horst Bischof In Multi-Camera Networks, Principles and Applications, pages 313-334, Academic Press, 2009
  4. Visual On-line Learning in Distributed Camera Networks (bib)Christian Leistner, Peter M. Roth, Helmut Grabner, Andreas Starzacher, Horst Bischof, and Bernhard Rinner 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

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