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

arXiv: https://arxiv.org/abs/2603.09235 · HTML: https://mlsanity.com/paper/2603.09235/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline](https://mlsanity.com/paper/2309.14611/) (2023)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [Asynchronous Blob Tracker for Event Cameras](https://mlsanity.com/paper/2307.10593/) (2023)
- [Data-Driven Feature Tracking for Event Cameras With and Without Frames](https://mlsanity.com/paper/2211.12826/) (2022)
- [MambaEVT: Event Stream based Visual Object Tracking using State Space Model](https://mlsanity.com/paper/2408.10487/) (2024)
- [Reliable Object Tracking by Multimodal Hybrid Feature Extraction and Transformer-Based Fusion](https://mlsanity.com/paper/2405.17903/) (2024)
- [SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features](https://mlsanity.com/paper/2309.16987/) (2023)
- [EEPPR: Event-based Estimation of Periodic Phenomena Rate using Correlation in 3D](https://mlsanity.com/paper/2408.06899/) (2024)

## Built on by (most cited first, 1 of 1)

- [Relative State Estimation using Event-Based Propeller Sensing](https://mlsanity.com/paper/2604.18289/) (2026)

## Abstract (arXiv)

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.