Horst Possegger

Authors: Possegger, Sternig, Mauthner, Roth, and Bischof

Combining foreground images from multiple views by projecting them onto a common ground-plane has been recently applied within many multi-object tracking approaches. These planar projections introduce severe artifacts and constrain most approaches to objects moving on a common 2D ground-plane. To overcome these limitations, we introduce the concept of an occupancy volume - exploiting the full geometry and the objects' center of mass - and develop an efficient algorithm for 3D object tracking. Individual objects are tracked using the local mass density scores within a particle filter based approach, constrained by a Voronoi partitioning between nearby trackers. We benefit from the geometric knowledge given by the occupancy volume to robustly extract features and train classifiers on-demand, when volumetric information becomes unreliable.

We evaluate our approach on several challenging real-world scenarios including the public APIDIS dataset. Experimental evaluations demonstrate significant improvements compared to state-of-the-art methods, while achieving real-time performance.

Authors: Possegger, Rüther, Sternig, Mauthner, Klopschitz, Roth, and Bischof

Pan-Tilt-Zoom (PTZ) cameras are widely used in video surveillance tasks. In particular, they can be used in combination with static cameras to provide high resolution imagery of interesting events in a scene on demand. Nevertheless, PTZ cameras only provide a single trajectory at a time. Hence, engineering algorithms for common computer vision tasks, such as automatic calibration or tracking, for camera networks including PTZ cameras is difficult. Therefore, we implemented a virtual PTZ (vPTZ) camera to simplify the algorithm development for such camera networks. The vPTZ camera is built on a cylindrical panoramic view of the scene and allows to reposition its field of view arbitrarily to provide several trajectories.

The vPTZ camera has been used to develop an unsupervised extrinsic self-calibration method for a network of static cameras and PTZ cameras, solely based on correspondences between tracks of a walking human. Our experimental results show that we can obtain accurate estimates of the extrinsic camera parameters in both, outdoor and indoor scenarios.