Automated analysis of surveillance videos has seen a lot of research in recent years. Face recognition and person tracking are widely available, more sophisticated behaviour analysis is coming. The aim of the current talk is an overview into the methods used for analysis, their current performance and limitations. Automated analysis of videos is a hot research topic currently, mostly fuelled (and funded) by interest in surveillance applications. Some of the work focuses on /identifying/ persons by individual differences in their motion patterns, e.g., the way the walk. Much current work tries to determine human interaction behaviour, e.g. whether two persons are standing and talking or whether they are fighting. A last big area is that of trajectory analysis, e.g. distinguishing persons walking straight across an open place from persons sticking around longer. This talk will give an overview into whats possible currently and then introduce some of the common methods of motion analysis with a focus on real-time capability. It will touch upon motion-history images, model-based tracking, graphical models for time-series and learning methods for classification. Throughout, pointers to toolkits that can be used to implement the methods presented will be provided. Of course, there are still a lot of problems, some of them quite fundamental, e.g. occlusions, crowds, influence of rain and wind, and the like. These problems, and their causes, will be explained under the assumption that the audience will be able to make creative use of this knowledge for playing with the system.
Secdocs is a project aimed to index high-quality IT security and hacking documents. These are fetched from multiple data sources: events, conferences and generally from interwebs.
Serving 8166 documents and 531.0 GB of hacking knowledge, indexed from 2419 authors from 163 security conferences.