METHODS FOR OBJECT DETECTION AND TRACKING IN VIDEO STREAMS FOR FLIGHT CONTROL SYSTEMS

Abstract

This article presents an improvement of object detection and tracking methods in video streams for flight control systems. The proposed approach is based on the introduction of adaptive processing mechanisms, including adaptive threshold filtering, spatial and morphological processing, flexible neural network classification, and tracking reinitialization.

The method is designed to operate under dynamic observation conditions characterized by illumination variations, background motion, noise, and limited computational resources. A comprehensive experimental study is conducted for different disturbance scenarios, including illumination changes, sensor noise, visually similar objects, and combined noise effects.

The evaluation focuses on detection accuracy, tracking stability, processing speed, and computational load distribution across processing stages. The findings confirm that the proposed method provides an effective balance between accuracy, robustness to disturbances, and computational efficiency, making it suitable for deployment in onboard video processing systems.