Recent interest has been shown in performance evaluation of visual surveillance systems. The main purpose of perfor-mance evaluation on computer vision systems is the statisti-cal testing and tuning in order to improve algorithm’s reli-ability 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 spe-cialized ground support vehicles. Robust detection of indi-viduals 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 eval-uation are illustrated and used to assess the performance of three motion segmentors on video sequences of an airport’s apron.
