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Designing static object detection systems that are able to incorporate user interaction conveys a great benefit in many surveillance applications, since some correctly detected static objects can be considered to have no interest by a human operator. Interactive systems allow the user to include these decisions into the system, making automated surveillance systems more attractive and comfortable...
Detecting static objects in video sequences has a high relevance in many surveillance scenarios like airports and railwaystations. In this paper we propose a system for the detection of static objects in crowded scenes that, based on the detection of two background models learning at different rates, classifies pixels with the help of a finite-state machine. The background is modelled by two mixtures...
Many learning and heuristic search algorithms require tuning of parameters to achieve optimum performance. In stationary and deterministic problem domains this is usually achieved through off-line sensitivity analysis. However, this method breaks down in non-stationary and non-deterministic environments, where the optimal set of values for the parameters keep changing over time. What is needed in...
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