I have successfully defended my PhD thesis "People Detection and Tracking in Crowded Scenes" at the Max Planck Institute for Informatics summa cum laude. Thesis Committee: Prof. Bernt Schiele, Prof. Michael Black, Prof. Luc Van Gool.
Winner of the CVPR 2017 Multi-Object Tracking Challenge (MOT17).
Four papers accepted at CVPR 2017!
Winner of the Multi-Object Tracking Challenge at CVPR 2017
Winner of the Multi-Object Tracking Challenge at ECCV 2016
BMVC Best Paper Award, 2012
Scholarship for excellence in academic performance RWTH Aachen 2009, 2010
SS 2016: High-Level Computer Vision, Saarland University, teaching assistant
SS 2015: High-Level Computer Vision, Saarland University, teaching assistant
SS 2013: High-Level Computer Vision, Saarland University, teaching assistant
Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B.
In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2016 (inproceedings)
This paper considers the task of articulated human pose estimation of multiple people in real-world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other.
This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems