Flight-Parameter-Based Motion Vector Prediction for Drone Video Compression

Block-based hybrid video coders typically use inter-prediction and bidirectionally coded (B) frames to improve compression efficiency. For this purpose, they employ look-ahead buffers, perform out-of-sequence frame coding, and implement similarity search-based general-purpose algorithms for motion estimation. While effective, these methods increase computational complexity and may not suit delay-sensitive practical applications such as real-time drone video transmission. If future motion can be predicted from external metadata, encoding can be optimized with lower complexity. In this study, a mathematical model for predicting motion vectors in drone video using only flight parameters is proposed. A remote-controlled drone with a fixed downward-facing camera recorded 4K video at 50 fps during autonomous flights over a marked terrain. Four flight parameters were varied independently, altitude, horizontal speed, vertical speed, and rotational rate. OpenCV was used to detect ground markers and compute motion vectors for temporal distances of 5 and 25 frames. Polynomial surface fitting was applied to derive motion models for translational, rotational, and elevational motion, which were later combined. The model was validated using complex motion scenarios (e.g., circular, ramp, helix), yielding worst-case prediction errors of approximately −1 ± 3 and −6 ± 14 pixels at 5 and 25 frames, respectively. The results suggest that flight-aware modeling enables accurate and low-complexity motion vector prediction for drone video coding.