# Event Camera Based Real-Time Detection and Tracking of Indoor Ground Robots

arXiv: https://arxiv.org/abs/2102.11916 · HTML: https://mlsanity.com/paper/2102.11916/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 86 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [Event-based, 6-DOF Camera Tracking from Photometric Depth Maps](https://mlsanity.com/paper/1607.03468/) (2016)
- [HFirst: A Temporal Approach to Object Recognition](https://mlsanity.com/paper/1508.01176/) (2015)
- [DART: Distribution Aware Retinal Transform for Event-based Cameras](https://mlsanity.com/paper/1710.10800/) (2017)
- [Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network](https://mlsanity.com/paper/1606.09433/) (2016)
- [Towards Low-Latency High-Bandwidth Control of Quadrotors using Event Cameras](https://mlsanity.com/paper/1911.04553/) (2019)
- [Novel Perception Algorithmic Framework For Object Identification and Tracking In Autonomous Navigation](https://mlsanity.com/paper/2006.04859/) (2020)

## Built on by (most cited first, 12 of 12)

- [CodedEvents: Optimal Point-Spread-Function Engineering for 3D-Tracking with Event Cameras](https://mlsanity.com/paper/2406.09409/) (2024)
- [A Survey of 3D Reconstruction with Event Cameras](https://mlsanity.com/paper/2505.08438/) (2025)
- [SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation](https://mlsanity.com/paper/2508.17643/) (2025)
- [Density Invariant Contrast Maximization for Neuromorphic Earth Observations](https://mlsanity.com/paper/2304.14125/) (2023)
- [High-temporal-resolution event-based vehicle detection and tracking](https://mlsanity.com/paper/2212.14289/) (2022)
- [Ultralight Polarity-Split Neuromorphic SNN for Event-Stream Super-Resolution](https://mlsanity.com/paper/2508.03244/) (2025)
- [NU-AIR -- A Neuromorphic Urban Aerial Dataset for Detection and Localization of Pedestrians and Vehicles](https://mlsanity.com/paper/2302.09429/) (2023)
- [Efficient Multi-Band Temporal Video Filter for Reducing Human-Robot Interaction](https://mlsanity.com/paper/2403.18096/) (2024)
- [Event-based Optical Marker Systems: A survey](https://mlsanity.com/paper/2504.20736/) (2025)
- [SAWEC: Sensing-Assisted Wireless Edge Computing](https://mlsanity.com/paper/2402.10021/) (2024)
- [EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time](https://mlsanity.com/paper/2411.11004/) (2024)
- [Real-time Unsupervised Object Discovery from Asynchronous Event Streams](https://mlsanity.com/paper/2608.26644/) (2026)

## Abstract (arXiv)

This paper presents a real-time method to detect and track multiple mobile ground robots using event cameras. The method uses density-based spatial clustering of applications with noise (DBSCAN) to detect the robots and a single k-dimensional (k - d) tree to accurately keep track of them as they move in an indoor arena. Robust detections and tracks are maintained in the face of event camera noise and lack of events (due to robots moving slowly or stopping). An off-the-shelf RGB camera-based tracking system was used to provide ground truth. Experiments including up to 4 robots are performed to study the effect of i) varying DBSCAN parameters, ii) the event accumulation time, iii) the number of robots in the arena, iv) the speed of the robots, and v) variation in ambient light conditions on the detection and tracking performance. The experimental results showed 100% detection and tracking fidelity in the face of event camera noise and robots stopping for tests involving up to 3 robots (and upwards of 93% for 4 robots). When the lighting conditions were varied, a graceful degradation in detection and tracking fidelity was observed.