Research Projects
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| Title | Abstract |
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EVis: Autonomous Traffic Monitoring by Embedded Vision
(details) |
The world will witness a tremendous increase in the number of vehicles in the near future. Future traffic monitoring systems will therefore play an important role to improve the throughput and safety of roads. Current monitoring systems capture (usually vision-based) traffic data from a large sensory network; however, they require continuous human supervision which is extremely expensive. In the proposed EVis research project we investigate the scientific and technological foundations for future autonomous traffic monitoring systems. Autonomy is achieved by a novel combination of three approaches: First, vision-based detection and classification methods are augmented by self-learning and scene adaptation mechanisms which will significantly reduce the effort of manual configuration. Second, visual data is fused with data from other sensors such as radar, infrared or inductive loop sensors. Sensor fusion helps to improve the robustness and confidence, to extend the spatial and temporal coverage as well as to reduce the ambiguity and uncertainty of the processed sensor data. Finally, the developed vision and fusion methods are implemented on a distributed embedded platform which makes them wider applicable and supports real-time operation. Our autonomous traffic monitoring system will be evaluated using real world traffic data. The evaluation will be conducted in three different case studies: offline testing using recorded data, online testing on a traffic test site, and on a test installation on a public road. |
2007 | 2010 |
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AUTOVISTA: Advanced Unsupervised Monitoring and Visualization of Complex Scenarios
(details) |
The trend in video surveillance is an ever increasing number of (digital) cameras for surveying complex scenarios (e.g. crowds). Currently available video surveillance systems cannot cope with this increased complexity, the detection rates are too low and the systems are not reliable enough. This hinders the broad use of automatic surveillance systems. AUTOVISTA proposes to use modern visual computing technologies to advance the state-of-the-art of video surveillance considerably. In order to cope with the increasing number of cameras, AUTOVISTA will (1) use novel on-line learning techniques to increase the detection rate and decrease the false alarm rate, while the camera adapts in an unsupervised manner to the surveyed scene. Besides an increased performance, this has the additional advantage that the installation and maintenance effort will be substantially decreased; (2) exploit novel visualization and interaction techniques to support the human operator. Furthermore two complementary visualization modes are proposed, blending smoothly between these allows the operator to maintain coherence. These techniques will enable a single operator to cope simultaneously with a large amount of cameras. AUTOVISTA will tackle the problem of increased people densities and highly cluttered scenes in a novel manner. Instead of relying on single person detection and tracking (which is not feasible for high people density scenarios), methods will be investigated to handle the crowd as a whole. AUTOVISTA will derive spatio-temporal crowd statistics, describe normal crowd behavior and use this for unusual event detection. |
2007 | 2009 |
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Computer Supported Automated Sports Game Analysis - Pilot Project Beachvolleyball
(details) |
The main aim of the project is to create a novel method for automated game analysis, which offers new insights in the structure of sport games. The first type of sport investigated will be beach volleyball which offers a not too complex structure(4 players, 1 ball and a rather small field) and is similar to several other types of sport.
All this functionality need to be offered in a software tool that can be used by sports scientists.
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2006 | 2008 |
