Centroiding Point-Objects With Event Cameras

Event-based sensors (EBS), with their low latency and high dynamic range, are a promising means for tracking unresolved point-objects. Conventional EBS centroiding methods assume the generated events follow a Gaussian distribution and require long event streams ($> 1$>1s) for accurate localization. However, these assumptions are inadequate for centroiding unresolved objects, since the EBS circuitry causes non-Gaussian event distributions, and because using long event streams negates the low-latency advantage of EBS. In this work, we derive a closed-form spatiotemporal event distribution that accounts for these non-Gaussian effects and relaxes the long-time window requirement. Using Fisher analysis, we show that the spatial distribution of events in short time windows ($\leq 20$≤20 ms) contains sufficient information for accurately estimating both position and velocity. To validate our analysis, we create the first EBS dataset of unresolved point-objects with subpixel ground truth using a high-speed monitor. We demonstrate that a small LSTM network can estimate an object's position within 1 pixel and velocity within $\pm 17\%$±17% using only 5 ms of event data, outperforming traditional approaches. These improvements enable accurate and quick centroiding of fast and dim objects, and we publish all code and data to support future research.