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Research in computer vision has made a remarkable impact in many real world situations and had provided robust solutions. For example, face detection algorithms have been integrated in most mass-consumer digital cameras. This has been made possible by the combination of robust feature extraction with statistical learning algorithms.In this project we want to similarly advance object detection and tracking by considering a challenging and complex scenario. Surgical work-flow recovery is crucial for designing context-sensitive service systems in future operating rooms. Abstract knowledge about actions which are being performed is particularly valuable in the operating room (OR). This knowledge can be used for many applications such as optimizing the work-flow, recovering average work-flows for guiding and evaluating training surgeons, automatic report generation and ultimately for monitoring in a context aware operating room. Our objective is to analyze the events happening in the medical operating room, equipped with multiple cameras, in order to prepare the necessary input for fully automatic work-flow analysis.