Disjoint Inter-Camera Tracking in the Context of Video-Surveillance

Disjoint intra-camera tracking is the task of tracking objects across video-surveillance cameras that have non-overlapping views. Disjoint intra-camera tracking is difficult due to the gaps in observation as an object moves between camera views. To solve this problem, an intra-camera video-surveillance system builds an appearance profile of the objects seen in its camera, and matches these appearance profiles to achieve the effect of tracking. This thesis demonstrates two novel ideas that improve Disjoint intra-camera tracking. The first is to use a Zernike moment based shape feature for objects observed in a scene, used to describe the shape of an object in a compact, reliable form. The second is to dynamically weigh the Zernike moment shape feature with other standard features to achieve better tracking results. Weighting emphasis is given to better features, more stable features, more recent values of features, and features that have been reliably translated from a different video camera

Fusion of Data from Heterogeneous Sensors with Distributed Fields of View and Situation Evaluation for Advanced Driver Assistance Systems

In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account