Leveraging On-Board UAV Motion Estimation for Lightweight Macroscopic Crowd Identification

This paper introduces a novel, lightweight, on-board approach to crowd pattern identification, ingeniously using the processes of existing video compression standards, particularly H.264 or MPEG-4. Piggy-backing on the H.264 video-encoding algorithm, we propose real-time crowd pattern recognition and identification methodologies that can identify macroscopic patterns in as low as 2 milliseconds on NVIDIA TX2, resulting in around 45 x execution time reduction compared to existing approaches. Furthermore, we introduce a temporally aware approach to pinpoint and adapt to crowd movement patterns, continuously recalibrating as a drone's Point Of View (POV) varies or observed motions diverge. Evaluating our method against publicly available datasets, we emphasize our system's performance and computational advantages, especially when faced with real-time observational shifts. In conclusion, our approach elegantly bridges the gap between crowd safety imperatives and the challenges of UAV monitoring, heralding a new era of real-time drone-centric crowd management intelligence.