The potential of graph-based methods in video tracking
This talk is about the two main topics of my research. Firstly, a method to extract a part-based model of an observed scene from a video sequence. It is based on the idea that things that move together throughout the whole video belong together and define a “rigid” object or part. A set of successfully tracked feature points is used for the necessary observations. By employing a graph pyramid, the feature points can be grouped depending on their motion over time. The result is a hierarchical description (graph pyramid) of the scene, where each vertex in the top level of the pyramid represents a “rigid” part of the foreground or the background, and encloses the salient features used to describe it. Secondly, an approach to track arbitrary objects in challenging scenes with simple trackers (e.g. Mean Shift). This is realized by describing and tracking the target object with a spring system represented by an attributed graph. A spring system encodes the spatial relationships of the features describing the target object and enforces them by spring-like behavior during tracking. Tracking is done in an iterative process by combining the hypotheses of simple trackers with the hypotheses extracted from the spring system.
speaker website: http://www.neolin.net/?page_id=474
Lab PRIP (TU Wien, Austria): http://www.prip.tuwien.ac.at/