Low Distance Airplanes Detection and Tracking Visually using Spectral Residual and KLT Composition
: Pervasive computing
Adaptive Image Adjusting,
This paper presents the method for detection and tracking airplanes which can be observed visually in low distances from sensors. They are used widely for some reasons such as military or unmanned aerial vehicle (UAV) because of their ability to hide from radar signals; however they can be detected and viewed by human eyes. Vision based methods are low cost and robust against jamming signals. Therefore, it is mandatory to have some visual approaches to detect airplanes. By this way, we propose spectral density for airplane detection and KLT algorithm for tracking. This approach is a hybrid of two distinct methods which have been presented by researchers and used widely in detection or tracking specific objects. To have accurate detection, image intensity would be adjusted adaptively. Correct detected airplanes would be achievable by eliminating some long optical flow trajectory in image frames. The proposed method would be analyzed and evaluated by comparison with state of the art approaches. The experimental results show the power of our approach in detection of multiple airplanes unless they become too small in presence of other objects and multiple airplanes. We make some test by implementing our approach on an useful database presented by some researchers.