# PUCK: Parallel Surface and Convolution-kernel Tracking for Event-Based Cameras

arXiv: https://arxiv.org/abs/2205.07657 · HTML: https://mlsanity.com/paper/2205.07657/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 6 of 6)

- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Real-time clustering and multi-target tracking using event-based sensors](https://mlsanity.com/paper/1807.02851/) (2018)
- [LuvHarris: A Practical Corner Detector for Event-cameras](https://mlsanity.com/paper/2105.11443/) (2021)
- [FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing](https://mlsanity.com/paper/2103.05903/) (2021)
- [e-TLD: Event-based Framework for Dynamic Object Tracking](https://mlsanity.com/paper/2009.00855/) (2020)
- [Efficient and Reactive Planning for High Speed Robot Air Hockey](https://mlsanity.com/paper/2107.06140/) (2021)

## Built on by (most cited first, 4 of 4)

- [Table tennis ball spin estimation with an event camera](https://mlsanity.com/paper/2404.09870/) (2024)
- [Training slow silicon neurons to control extremely fast robots with spiking reinforcement learning](https://mlsanity.com/paper/2601.21548/) (2026)
- [Efficient Multi-Timescale Event Representations for Feed-Forward Object Detection](https://mlsanity.com/paper/2609.05049/) (2026)
- [Can Spiking Neural Networks play pinball? A neuromorphic motion detector for target tracking](https://mlsanity.com/paper/2609.24403/) (2026)

## Abstract (arXiv)

Low latency and accuracy are fundamental requirements when vision is integrated in robots for high-speed interaction with targets, since they affect system reliability and stability. In such a scenario, the choice of the sensor and algorithms is important for the entire control loop. The technology of event-cameras can guarantee fast visual sensing in dynamic environments, but requires a tracking algorithm that can keep up with the high data rate induced by the robot ego-motion while maintaining accuracy and robustness to distractors. In this paper, we introduce a novel tracking method that leverages the Exponential Reduced Ordinal Surface (EROS) data representation to decouple event-by-event processing and tracking computation. The latter is performed using convolution kernels to detect and follow a circular target moving on a plane. To benchmark state-of-the-art event-based tracking, we propose the task of tracking the air hockey puck sliding on a surface, with the future aim of controlling the iCub robot to reach the target precisely and on time. Experimental results demonstrate that our algorithm achieves the best compromise between low latency and tracking accuracy both when the robot is still and when moving.