Effectiveness of Knowledge Models for Visual Object Detection

There are various effective methods of image processing for detecting moving objects from video information, such as background subtraction, optical flow, and edge extraction. Some image processing techniques such as image difference detection can detect targets small pixels. On the other hand, these methods do not recognize and distinguish the target itself. Image recognition such as machine learning can also detect objects from video information. However, if there are not enough pixels to recognize the target, it is difficult to find it by image recognition. In this study, we tried to develop a mechanism for automatic detecting function of aircraft approaching to the airport by using visual cameras. In the situation where aircraft flying to the airport for landing, the image of aircraft appears as a small dot. And, this target image does not have enough pixel information to identify the aircraft by image recognition techniques. However, a tower controller can identify whether a small dot is an aircraft or not under the similar condition. This is because air traffic controllers make their decisions based on their operational knowledge and experience. Therefore, we aim to model the knowledge and experience of air traffic controllers explicitly to judge the situation as rules for high accuracy target detection.In order to detect and track a specific object continuously, another mechanism is needed for identifying the object. In this study, we propose a rule-based model as an air traffic controllers' operational knowledge for detecting the target from the frame of video image. We present techniques for the detection and selection of target aircraft that appear as small dots on the image. And then, we discuss the results of our validation of the effectiveness of our proposed approach.