Skip to main content
Article thumbnail
Location of Repository

Obstacle detection in aerodrome areas through the use of computer vision

By Jason Gauci


This thesis addresses the problem of ground collisions between an aircraft and obstacles (including other aircraft) on the ramp and taxiway regions of an aireld. A safety study is conducted by looking at current operating procedures and analysing accident statistics and reports. An onboard non-collaborative system for large transport aircraft is proposed and its main requirements and performance characteristics are discussed. The main requirement is to detect and track generic obstacles around an aircraft during taxi manoeuvres. The suitability of computer vision to the application of interest of this work is investigated through comparison with other candidate sensor technologies and computer vision, using visible cameras, is selected as the preferred technology. A study of dierent optical solutions is carried out and stereo vision is considered to be the most suitable choice. Two locations on the aircraft are considered for camera installation and the installation of a stereo vision system on each wingtip is chosen. Algorithms are implemented for the dierent processing blocks of the stereo vision system. These comprise calibration, rectication, correspondence, reconstruction, detection, clustering, and tracking algorithms. For each process, existing methods and techniques are reviewed and the most appropriate ones are selected, modied and improved in order to meet the specic requirements of this application. The values of several parameters of each algorithm are found experimentally using synthetic data and each algorithm is tested individually before being integrated with the rest of the system. Overall system performance is evaluated by testing for positional accuracy, generic obstacle detection and tracking capabilities, and sensitivity to calibration errors. Testing is conducted for a range of realistic conict scenarios, under dierent illumination, visibility, and image noise conditions. Both synthetic images and real images are used. The results of both sets of images are compared and these suggest that the stereo vision system developed in this research has the potential to reduce wingtip collisions and can therefore improve safety and situational awareness in aerodrome areas

Publisher: Cranfield University
Year: 2010
OAI identifier:
Provided by: Cranfield CERES

Suggested articles


  1. (2002). A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. doi
  2. (2007). Adaptive extended kalman for visual motion estimation of 3d objects. doi
  3. (1976). Adaptive sequential estimation with unknown noise statistics. doi
  4. (2009). Automatic Dependent Surveillance-Broadcast (ADS-B). doi
  5. (2003). Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond.
  6. (2007). Detection and Tracking of Moving Vehicles in Crowded Scenes. doi
  7. (1998). GOLD: A Parallel Real-Time Stereo Vision System for Generic Obstacle and Lane Detection. doi
  8. (1999). Image stabilization by features tracking. doi
  9. (2006). Obstacle Avoidance For Unmanned Air Vehicles Using Image Feature Tracking. doi
  10. (2004). Using Synchronised FireWire Cameras For Multiple Viewpoint Digital Video Capture.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.