HelixTrack: Event-Based Tracking and RPM Estimation of Propeller-like Objects

TL;DR

This work introduces the Timestamped Quadcopter with Egomotion (TQE) dataset with 13 high-resolution event sequences, containing 52 rotating objects in total, captured at distances of 2 m / 4 m, with increasing egomotion and microsecond RPM ground truth.

Abstract

Safety-critical perception for unmanned aerial vehicles and rotating machinery requires microsecond-latency tracking of fast, periodic motion under egomotion and strong distractors. Frame-based and event-based trackers drift or break on propellers because periodic signatures violate their smooth-motion assumptions. We tackle this gap with HelixTrack, a fully event-driven method that jointly tracks propeller-like objects and estimates their rotations per minute (RPM). Incoming events are back-warped from the image plane into the rotor plane via a homography estimated on the fly. A Kalman Filter maintains instantaneous estimates of phase. Batched iterative updates refine the object pose by coupling phase residuals to geometry. To our knowledge, no public dataset targets joint tracking and RPM estimation of propeller-like objects. We therefore introduce the Timestamped Quadcopter with Egomotion (TQE) dataset with 13 high-resolution event sequences, containing 52 rotating objects in total, captured at distances of 2 m / 4 m, with increasing egomotion and microsecond RPM ground truth. On TQE, HelixTrack processes full-rate events (approx. 11.8x real time) faster than real time and microsecond latency. It consistently outperforms per-event and aggregation-based baselines adapted for RPM estimation.

Figures (6)

• Figure 1: HelixTrack -- tracking and instantaneous rotations per minute estimation of propeller-like objects from asynchronous events. Incoming events are back‑warped from the image plane "$\mathbf{z}$" into the rotor plane "$\mathbf{u}$" by a homography. Rotor‑plane coordinates are gated with $r_\text{in}$ and $r_\text{out}$ to a ring‑shaped band and used for: (i) per-event phase update via Kalman filter, and (ii) batched pose refinement via iterative homography update.
• Figure 2: Aggregated events from two sequences in the Timestamped Quadcopter with Egomotion dataset. Top row: smallest egomotion ($M_{\text{ego}}=1$); bottom row: largest egomotion ($M_{\text{ego}}=7$). Events are aggregated from the first 10 ms of five 1-second intervals.
• Figure 3: Projection of per‑propeller unit circles under the final homography estimate, overlaid with HelixTrack trajectories on selected Timestamped Quadcopter with Egomotion sequences. Propellers are color‑coded: rear left green, rear right orange, front left red, front right cyan. Markers encode time: size increases and opacity decreases backward in time, so older states appear larger and more transparent.
• Figure 4: Robustness to initialization errors. We perturb the initial tracker position, in‑plane scale, and initial RPM by $\pm \{1,2,\dots,100\}\%$ around the nominal initialization and run HelixTrack end‑to‑end. For position, we average shifts "toward" and "away from" the nearest distractor (propeller). The plot reports shaft‑RPM Mean Absolute Error (MAE) across propellers with MAE $\leq 300$ (lower is better); red line marks 10,000 RPM.
• Figure S1: Aggregated events from all sequences in the Timestamped Quadcopter with Egomotion dataset with camera-to-quadcopter distance of 2 m, from the smallest (top row, $M_{\text{ego}}$=1), to the largest (bottom row, $M_\text{ego}$=7) egomotion intensity. Events from the first 10 ms of five 1-second intervals are shown. Positive events displayed in black, negative in gray.