Interface architecture of a modular video analysis platform for autonomous uav control

Abstract

The purpose of this work is to present the interface architecture of a modular software platform for video analysis in autonomous UAV control systems, with emphasis on extensibility and component replaceability.
The platform is organized around three independent modularity levels. The first is the video source contract (CameraSource): regardless of the camera type, each source runs a background capture thread and writes frames to a shared buffer. Three implementations are available – USBCameraSource via OpenCV VideoCapture, PiCameraSource using the Picamera2 library for Raspberry Pi CSI cameras (including infrared models), and VideoFileSource for offline development with recorded footage. Switching between them requires changing a single line in the server configuration. The second level is the frame processing contract (FrameProcessor), built on duck typing rather than formal inheritance. A researcher implements one mandatory method – photo_processing(frame) – which receives the current BGR frame as a NumPy array. Three optional methods extend the interaction: catch_cmd(roi) is called when the operator clicks on the video stream to select a region of interest; customize_processing(id) responds to one of four control panel buttons, enabling in-flight algorithm parameterization; getPreparedVector() closes the autonomous control loop by returning velocity components vx, vy and execution time t. The third level covers flight controller connectivity: the FlightController class wraps MAVSDK and accepts a MAVLink connection string as its only configuration parameter, enabling transparent switching between Pixhawk, SpeedyBee, Matek hardware and the SITL software simulator. Four FrameProcessor implementations – a CSRT tracker with SIFT initialization, SIFT homography-based position holding, YOLO object detection, and a minimal template – were integrated and validated on the platform, each implemented as a single class without touching any other component.
The proposed architecture lowers the barrier for integrating new video processing algorithms to implementing a minimal class interface. A researcher working with the platform needs to focus only on the algorithm itself – camera handling, telemetry logging, and flight command execution are already provided. This makes the platform a practical and reproducible baseline for vision-based UAV autonomy research.