Abstract:
Tracking of a particular object in an image using the feature tracking algorithms faces significant
challenges under conditions of severe aircraft rotation, highly cluttered background presence,
arrival and collision with other aircrafts and sun, excessive noise, and varying lightening
conditions due to weather changes. In this research, a real-time and robust algorithm is presented
for the tracking of an aircraft in video sequences of low resolution with the use of Kanade-LucasTomasi (KLT) feature tracker, random sample consensus (RANSAC) algorithm, texture
modeling and matching techniques. Features of an aircraft are tracked using KLT feature tracker
while developing aircraft model at each frame and finding transition matrix with the use of each
pair of consecutive frames. Features of aircrafts are excluded when not following the transition
of the aircraft and new features are introduced if they are within the bounding range of the
aircraft and match with the current aircraft model. The varying weather conditions and noise
reduction are handled in the algorithm without the use of averaging filters to increase the overall
efficiency of the framework.
