In this paper, we present a computationally efficient method for real-time detection and state estimation of quadcopter propellers in high-resolution event-camera streams. We model local event arrivals as Poisson processes and exploit the memoryless nature of inter-arrival times to robustly detect periodic bursts from rotating blades, even at high rotational speeds. Unlike approaches that process data in chunks, our method updates the detection metrics for each incoming event. Once a propeller is detected, we first calculate its angular speed and then fit an ellipse to the aggregated propeller events to estimate pitch and roll. We introduce a new dataset (speeds 1100–8200 RPM; tilt angles 0 \(^\circ \) , 10 \(^\circ \) , and 90 \(^\circ \) ) and achieve near-perfect detection accuracy at an average real-time factor of 0.94 on a single CPU core, demonstrating the suitability of the approach for onboard deployment.