Phone +43 316 873-5074
Office Room ID02050
I received an MSc degree (Dipl.-Ing.) and a PhD degree (Dr.techn.) in Telematics from the Graz University of Technology in 2009 and 2014, respectively. Currently, I am a postdoctoral researcher at the Institute for Computer Graphics and Vision where I have been involved in the Person ReID, the OUTLIER, the KiwiVision Cloud, and the MobiTrick project. My research is focused on inter-camera person re-identification via (on-line) machine learning considering different feature representations.
- Inter-camera person re-identification
- Metric learning
- Machine learning in general
- Feature representations
Relaxed Pairwise Learned Metric for Person Re-Identification, ECCV 2012
(Hirzer, Roth, Koestinger, Bischof)
Matching persons across non-overlapping cameras is a rather challenging task. Thus, successful methods often build on complex feature representations or sophisticated learners. A recent trend to tackle this problem is to use metric learning to find a suitable space for matching samples from different cameras. However, most of these approaches ignore the transition from one camera to the other. In this paper, we propose to learn a metric from pairs of samples from different cameras. In this way, even less sophisticated features describing color and texture information are sufficient for finally getting state-of-the-art classification results. Moreover, once the metric has been learned, only linear projections are necessary at search time, where a simple nearest neighbor classification is performed. The approach is demonstrated on three publicly available datasets of different complexity, where it can be seen that state-of-the-art results can be obtained at much lower computational costs.
The work was supported by the Austrian Science Foundation (FWF) project Advanced Learning for Tracking and Detection in Medical Workflow Analysis (I535-N23) and by the Austrian Research Promotion Agency (FFG) project SHARE in the IV2Splus program.
Person Re-Identification by Efficient Impostor-based Metric Learning, AVSS 2012
(Hirzer, Roth, Bischof)
Recognizing persons over a system of disjunct cameras is a hard task for human operators and even harder for automated systems. In particular, realistic setups show difficulties such as different camera angles or different camera properties. Additionally, also the appearance of exactly the same person can change dramatically due to different views (e.g., frontal/back) of carried objects. In this paper, we mainly address the first problem by learning the transition from one camera to the other. This is realized by learning a Mahalanobis metric using pairs of labeled samples from different cameras. Building on the ideas of Large Margin Nearest Neighbor classification, we obtain a more efficient solution which additionally provides much better generalization properties. To demonstrate these benefits, we run experiments on three different publicly available datasets, showing state-of-the-art or even better results; however, on much lower computational efforts. This is in particular interesting since we use quite simple color and texture features, whereas other approaches build on rather complex image descriptions!
The work was supported by the Austrian Research Promotion Agency (FFG) within the project SHARE in the IV2Splus program.
Person Re-Identification by Descriptive and Discriminative Classification, SCIA 2011
(Hirzer, Beleznai, Roth, Bischof)
Person re-identification, i.e., recognizing a single person across spatially disjoint cameras, is an important task in visual surveillance. Existing approaches either try to find a suitable description of the appearance or learn a discriminative model. Since these different representational strategies capture a large extent of complementary information we propose to combine both approaches. First, given a specific query, we rank all samples according to a feature-based similarity, where appearance is modeled by a set of region covariance descriptors. Next, a discriminative model is learned using boosting for feature selection, which provides a more specific classifier. The proposed approach is demonstrated on two datasets, where we show that the combination of a generic descriptive statistical model and a discriminatively learned feature-based model attains considerably better results than the individual models alone. In addition, we give a comparison to the state-of-the-art on a publicly available benchmark dataset.
This work has been supported by Siemens AG Österreich, Corporate Technology (CT T CEE), Austria, and the project SECRET (821690) under the Austrian Security Research Programme KIRAS.
- Multiple Model Fitting by Evolutionary Dynamics (bib) In Proc. IEEE International Conference on Pattern Recognition (ICPR), 2014
Mahalanobis Distance Learning for Person Re-Identification
In Person Re-Identification, pages 247-267, Springer, 2014
(The original publication is available at www.springer.com)
- Combining Descriptive and Discriminative Information for Person Re-Identification (bib) Ph.D. Thesis, Graz University of Technology, Faculty of Computer Science, 2014
- Pedestrian Detection, Tracking and Re-Identification for Search in Visual Surveillance Data (bib) In Proc. Conf. of the Hungarian Association for Image Processing and Pattern Recognition, 2013
- Dense Appearance Modeling and Efficient Learning of Camera Transitions for Person Re-Identification (bib) In Proc. IEEE International Conference on Image Processing (ICIP), 2012
- Person Re-Identification by Efficient Impostor-based Metric Learning (bib) In Proc. IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2012
Relaxed Pairwise Learned Metric for Person Re-Identification
In Proc. European Conference on Computer Vision (ECCV), 2012
(The original publication is available at www.springerlink.com)
- Large Scale Metric Learning from Equivalence Constraints (bib) In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012
Person Re-Identification by Descriptive and Discriminative Classification
In Proc. Scandinavian Conference on Image Analysis (SCIA), 2011
(The original publication is available at www.springerlink.com)
- Multi-Cue Learning and Visualization of Unusual Events (bib) In Proc. 11th IEEE Workshop on Visual Surveillance (ICCV), 2011
- Saliency Driven Total Variation Segmentation (bib) In Proc. International Conference on Computer Vision, 2009
- An Automatic Hybrid Segmentation Approach for Aligned Face Portrait Images (bib) In Proc. Workshop of the Austrian Association for Pattern Recognition, 2009
- Marker Detection for Augmented Reality Applications (bib) Technical Report, Graz University of Technology, Inst. f. Computer Graphics and Vision, ICG-TR-08/05, 2008
- Segmentation of Face Images (bib) MSc. Thesis, Graz University of Technology, Faculty of Computer Science, 2008