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- KurzbeschreibungIn the context of intelligent surveillance of public facilities, the automatic analysis of persons by distributed sensor systems increasingly gains in importance. The detection of hazardous material in busy areas as well as its assignment to a person is a challenging task that cannot be performed without technical decision support. However, the application of conventional technologies and the corresponding courses of action lead to long waiting times and pressure of work for the security personnel. This situation can be extremely relieved by security assistance systems with the ability to analyze an area automatically, based on distributed sensor systems and appropriate data fusion techniques.<br>This thesis makes contributions to the design and the realization of a security assistance system. The system aims at localizing a person with hazardous material in a person stream. To this end, the design stipulates two complementary types of sensors: tracking sensors and sensors to detect hazardous material. While the first type provides location data of high accuracy, but does not have any detection abilities, the second type is able to signalize the presence of a material, but is not able to localize the source. Only within a data fusion approach combining the strengths of the two types of sensor technologies, it is possible to distinguish the carrier of a material from the non-carriers in the person stream. This thesis is particularly focussed on the design of data fusion techniques to realize the described system task. In the context of this task, a probabilistic framework for person tracking and classification in well-defined areas is developed.<br>To realize the tracking component, a novel approach for tracking multiple persons as extended objects is developed. Within this approach ellipsoidal object extents are modeled by random matrices and treated as additional state variables to be estimated. The unknown object extent is accommodated through the use of a Wishart prior on the measurement probability density. An extended object can either be given by a single person due to high-resolution sensor data, or by a group of persons with correlated movements. Methodically considered, the new approach is derived by integrating random matrices with the framework of Probabilistic Multi-Hypothesis Tracking (PMHT). The new algorithm is called the PMHT for Extended Objects (PMHT-E). Besides the derivation and the statement of the basic PMHT-E, several useful extensions are introduced in this thesis. This includes the derivation of the Histogram PMHT-E for tracking extended objects in image sequences and the design of a track management system (TMS)
- AutorMonika Wieneke
- VerlagGca Ges.F.Computeranw.
- Seiten288 Seiten
- Gewicht455 g
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