Using intelligent digital cameras to monitor aerodrome surface traffic

An aircraft is most at risk for an accident when it's still on the ground - when taxiing before takeoff or after landing. This is because traffic throughput on the ground is limited by inadequate airport infrastructures and is often incapacitated during conditions of poor visibility. The INTERVUSE project, funded by the European Commission, aims to address these problems by developing a cost-effective artificial intelligence system based on a network of intelligent digital cameras. The system uses image-processing techniques to detect traffic and correlates and fuses data to generate a synthetic ground-situation display.

[1]  Gian Luca Foresti.  A real-time system for video surveillance of unattended outdoor environments , 1998, IEEE Trans. Circuits Syst. Video Technol..

[2]  J. Garcia,et al.  Automatic video system for aircraft identification , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[3]  Ian D. Reid,et al.  The Active Recovery of 3D Motion Trajectories and Their Use in Prediction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Takeo Kanade,et al.  Algorithms for cooperative multisensor surveillance , 2001, Proc. IEEE.

[5]  P. G. Michalopoulos,et al.  Vehicle detection video through image processing: the Autoscope system , 1991 .

[6]  R D Jacobson,et al.  AUTOMATIC INCIDENT DETECTION THROUGH VIDEO IMAGE PROCESSING , 1993 .

[7]  Hans-Hellmut Nagel,et al.  Incremental recognition of traffic situations from video image sequences , 2000, Image Vis. Comput..

[8]  Stephen J. Maybank,et al.  Visual Surveillance for Moving Vehicles , 1998, International Journal of Computer Vision.

[9]  Michael J. Brooks,et al.  Issues in Automated Visual Surveillance , 2003, DICTA.

[10]  José M. Molina López,et al.  Cooperative management of a net of intelligent surveillance agent sensors , 2003, Int. J. Intell. Syst..

[11]  Jesús García,et al.  Aircraft identification integrated into an airport surface surveillance video system , 2004, Machine Vision and Applications.

[12]  D P Panda.  DEPLOYMENT OF THE NEXT GENERATION MACHINE VISION TECHNOLOGY AND INTEGRATION WITH SCOOT IN THE CITY OF MINNEAPOLIS , 1998 .

[13]  Stuart J. Russell,et al.  Object Identification: A Bayesian Analysis with Application to Traffic Surveillance , 1998, Artif. Intell..

[14]  Prof. Niovi Pavlidou.  IST INTERVUSE PROJECT : INTEGRATED RADAR , FLIGHT PLAN AND DIGITAL VIDEO DATA FUSION FOR A-SMGCS , 2004 .

[15]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[16]  Charmaine Cisneros Franck,et al.  Evaluation of FLIR/IR camera technology for airport surface surveillance , 1996, Defense, Security, and Sensing.