Autonomous Virtual Agents for Performance Evaluation of Tracking Algorithms
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
The SAFEE on-board threat detection system
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Understanding transit scenes: a survey on human behavior-recognition algorithms
IEEE Transactions on Intelligent Transportation Systems
Multi-object tracking evaluated on sparse events
Multimedia Tools and Applications
Multimedia Tools and Applications
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Recent interest has been shown in performance evaluation of visual surveillance systems. The main purpose of performance 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.