Learning to Detect Aircraft at Low Resolutions

Abstract

An application of the Viola and Jones object detector to the problem of aircraft detection is presented. This approach is based on machine learning rather than morphological filtering which was mainly used in previous works. Aircraft detection using computer vision methods is a challenging problem since target aircraft can vary from subpixels to a few pixels in size and the background can be heavily cluttered. Such a system can be a part of a collision avoidance system to warn the pilots of potential collisions. Initial results suggest that this (static) approach on a frame to frame basis achieves a detection rate of about 80% and a false positive rate which is comparable with other approaches that use morphological filtering followed by a tracking stage. The system was evaluated on over 15000 frames which were extracted from real video sequences recorded by NASA and has the potential of real time performance.