Research Projects (2009)

Different authorities like such as the Ministry of the Interior often require to find certain event or behavior patterns in recordings in large video archives. This "forensic" search is computationally extremely expensive and due to restricted storage permissions often even not possible. Thus, security-critical events can often not prevented or being postpursued. To overcome these problems, the aim of the OUTLIER project is the investigation of algorithms, methods, and processes to alleviate the work of security staff in searching and pursuiting of events in video archives. Furthermore these tasks should be performed more efficiently.

Based on the requirements of the Ministry of the Interior as well as the possibilities of an infrastructure operator these issues should be examined and a research prototype should be created. This should occur in cooperation of AIT and ICG (University of Technology Graz) as research partners and ASE as an industrial partner. Essential research subjects are: (i) detection and segmentation of people, (ii) comparisons and finding of events in different video streams, and (iii) analyses and learning of behavior patterns. In addition, a social-scientific acceptance research will be established by the research institute of the Red Cross (FRK). Based on these results recommendations are compiled for the optimization by use and minimization of problem potentials from social-scientific view.

The ever increasing number of cameras in surveillance system requires automatic video analysis in order to spot critical situations and to alert the monitoring personnel in a timely manner. While most current approaches in this area aim for detecting a large number of specific events on a large set of complex application scenarios, the goal of this project is to go far beyond state of the art by developing novel online learning methods to detect unusual situations in a camera specific scenario. We will exploit the huge amount of data available for a specific camera to reliably learn usual and unusual situations.

In particular the OUTLIER project will carry out basic research in the following areas:

• Improved unsupervised learning methods for huge amounts of data
• Novel methods for semi-supervised learning in huge amounts of unlabeled data

These generic learning algorithms will be applied for the detection of unusual situations in public places and traffic scenarios. Examples are the detection of unusual crowd behavior (upcoming panic, barred emergency exits, or toppled persons), suspicious behavior of pedestrians (e.g. going from one car to another, loitering), vehicles or persons moving on unusual locations, the detection of unusual types of moving objects and detection of unusual situations like accidents, clashes and collisions. Unlike other approaches we do not want to model these situations explicitly and individually, but we will resort to learning to discriminate the usual situation from the unusual one.

Research partners in the project are JRS, TUG for basic and applied research and Siemens for industrial exploitation of project results.