FOREGROUND DETECTION IN DYNAMIC SCENES OF INTELLIGENT TRANSPORT SYSTEMS - Kharchenko, Volodymyr; Kuzmenko, Nataliia; Kukush, Alexander (Proceedings of National Aviation University (archive), 2019)
The paper considers aspects of foreground detection in dynamic scenes of intelligent transport system based on computer vision and artificial intelligence. Traditional and recent background modeling models have been considered. Nonparametric approach for background subtraction based on the kernel model was used as the most appropriate in dynamic environment. The value of the estimated kernel density function for each pixel of original image was compared with threshold value, estimated by Otsu’s method. The proposed kernel density estimation method was verified on video-stream containing moving objects and indicated good performance for Unmanned Aerial Vehicles application.