Performance evaluation of video object tracking algorithm in autonomous surveillance system

Results of a performance evaluation of a video object tracking algorithm are presented. The method of moving object detection and tracking is based on background modelling with mixtures of Gaussian and Kalman filters. An emphasis is put on algorithm's efficiency with regards to its settings. Utilized methods of a performance evaluation based on a comparison of the algorithm output to manually prepared reference data are introduced. The experiments aimed at examining the performance achieved with various object detection algorithm parameter settings are presented and discussed.

[1]  Piotr Dalka,et al.  Detection and segmentation of moving vehicles and trains using Gaussian mixtures, shadow detection and morphological processing , 2006 .

[2]  Andrzej Czyzewski,et al.  Examining Kalman Filters Applied to Tracking Objects in Motion , 2008, 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services.

[3]  David S. Doermann,et al.  Tools and techniques for video performance evaluation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[4]  Andrzej Czyzewski,et al.  Surveillance Camera Tracking of Geo positioned Objects , 2009, KES IIMSS.

[5]  Rita Cucchiara,et al.  Video Surveillance Online Repository (ViSOR): an integrated framework , 2010, Multimedia Tools and Applications.

[6]  Jin Hyeong Park,et al.  Performance evaluation of object detection algorithms , 2002, Object recognition supported by user interaction for service robots.

[7]  Moving Object Detection and Tracking for the Purpose of Multimodal Surveillance System in Urban Areas , 2008, New Directions in Intelligent Interactive Multimedia.