Effectiveness Of Moving Objects Detecting And Tracking In Airspace By Images In NearInfrared

Abstract

Objects in nearinfrared (NIR) images can often have different linear scales and shapes than the same objects in optical images for visible spectrum (RedGreenBlue, RGB). Therefore they can require different computer vision methods for detection, tracking, and classification. This paper devoted to the methods by which the problems of moving objects detecting and tracking in NIR images are solved. The main characteristics of moving objects in image sequences are highlighted. Advantages and disadvantages of different methods for detecting and tracking objects in NIR images of airspace are considered and two of the most promising methods classes are selected. Studies have been carried out on the effectiveness of LucasKanade method, which is one of the methods of local optical flow, and the ORB method of scaleinvariant transformation of features when detecting and tracking moving objects in NIR images. In numerical experiments, more than 5000 NIR images containing moving objects of three types were used as well as three combinations of considered methods. It is shown which combination is the most accurate in the tasks of moving objects detection and tracking and can be used for airspace automatic operational control and management based on computer vision systems. Probably, other combinations of methods from the two considered classes also can help to increase accuracy of moving objects detection and tracking in airspace by NIR images.