Prof. Dr.-Ing. Laura Leal-Taixé was born in Barcelona where she pursued B.Sc. and M.Sc. degrees in Telecommunications Engineering at the Technical University of Catalonia (UPC). She went to Boston, USA to do her Master’s thesis at Northeastern University with a fellowship from the Vodafone foundation. She worked on computer vision and image processing for medical images. From 2009 until 2013, she worked towards her PhD degree at the Institute for Information Processing (TNT) of the Leibniz University of Hannover in Germany. She focused on topics such as multiple people tracking, object detection, pose estimation. She graduated in February 2014, presenting the thesis “Multiple people tracking with context awareness”. During her PhD, she was at the University of Michigan, Ann Arbor, USA, for one year as a visiting scholar with Prof. Silvio Savarese. She then spent two years as a postdoctoral researcher at the Institute for Geodesy and Photogrammetry at ETH Zürich, Switzerland, working on tracking and benchmarking.
In May 2016, she moved to Munich where she started working at the Computer Vision Group as a senior postdoctoral researcher.
She is currently as Professor at the Dynamic Vision and Learning group after receiving a Sofja Kovalevskaja Award of 1.65 million euros for her project socialMaps.
2017 Sofja Kovalevskaja Award, 1.65 million euros, Humboldt Foundation
2017 DAAD Funding, Australia-German Joint Research Corporation Scheme
2016 Travel grant awarded by the Women in Computer Vision Association at CVPR
2013 Travel grant awarded for the Doctoral Consortium at CVPR
2008 Vodafone scholarship to pursue the Master Thesis in the USA
Prof. Dr.-Ing. Laura Leal-Taixé (b. 1984) conducts research in the area of Computer Vision and Machine Learning. In particular, she focuses on video analysis, solving tasks such as multiple object tracking, motion analysis or semantic segmentation. Allowing machines to automatically analyse video data is essential for applications such as Autonomous Driving.
During her doctoral studies, she focused on including social and multi-view information into optimization schemes for multiple pedestrian tracking. She later moved towards Machine Learning and how to better adapt learning algorithms such as neural networks to work on video streams.
In the project socialMaps, for which she has been granted a Sofja Kovalevskaja Award, she proposes to include dynamic and social information into static maps, so as to decouple vehicle traffic from pedestrian traffic.
- Continuous Pose Estimation with a Spatial Ensemble of Fisher Regressors. 2015 IEEE International Conference on Computer Vision (ICCV), IEEE, 2015
- Joint tracking and segmentation of multiple targets. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2015
- Learning an Image-Based Motion Context for Multiple People Tracking. 2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2014
- Class Generative Models Based on Feature Regression for Pose Estimation of Object Categories. 2013 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2013
- Branch-and-price global optimization for multi-view multi-target tracking. 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2012
- Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker. 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), IEEE, 2011