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Recent interest has been shown in performance evaluation of visual surveillance systems. The main purpose ofperformance evaluation on computer vision systems is the statistical testing and tuning in order to improve algorithm's reliability and robustness. In this paper we investigate the use of empirical discrepancy metrics for quantitative analysis of motion segmentation algorithms. We are concerned with the case of visual surveillance on an airport's apron, that is the area where aircrafts are parked and serviced by specialized ground support vehicles. Robust detection of individuals and vehicles is of major concern for the purpose of tracking objects and understanding the scene. In this paper, different discrepancy metrics for motion segmentation evaluation are illustrated and used to assess the performance of three motion segmentors on video sequences of an airport's apron.

In a football stadium environment with multiple overhead floodlights, many protruding shadows can be observed originating from each of the targets. To successfully track individual targets, it is essential to achieve an accurate representation of the foreground. Unfortunately, many of the existing techniques are sensitive to shadows, falsely classifying them as foreground. In this work an unsupervised learning procedure that determines the RGB colour distributions of the foreground and shadow classes of feature data is proposed. A novel skelatonisation and spatial filtering process is developed for identifying components in the foreground segmentation that are most-likely to belong to each class of feature. A pixel classification mechanism is obtained at by approximating both classes of feature data by N Gaussian parametric models. To assess our technique's performance and reliability, a comparison is made with other published works.

Abstract. This paper presents a complete visual surveillance system for automatic scene interpretation of airport aprons. The system comprises two main modules -Scene Tracking and Scene Understanding. The Scene Tracking module is responsible for detecting, tracking and classifying the semantic objects within the scene using computer vision. The Scene Understanding module performs high level interpretation of the observed objects by detecting video events using cognitive vision techniques based on spatio-temporal reasoning. The performance of the system is evaluated for a series of pre-defined video events specified using a video event ontology.