Event-based cameras have become increasingly popular for tracking fast-moving objects due to their high temporal resolution, low latency, and high dynamic range. In this paper, we propose a novel algorithm for tracking event blobs using raw events asynchronously in real time. We introduce the concept of an event blob as a spatio-temporal likelihood of event occurrence where the conditional spatial likelihood is blob-like. Many real-world objects generate event blob data, for example, flickering LEDs such as car headlights or any small foreground object moving against a static or slowly varying background. The proposed algorithm uses a nearest neighbour classifier with a dynamic threshold criteria for data association coupled with a Kalman filter to track the event blob state. Our algorithm achieves highly accurate tracking and event blob shape estimation even under challenging lighting conditions and high-speed motions. The microsecond time resolution achieved means that the filter output can be used to derive secondary information such as time-to-contact or range estimation, that will enable applications to real-world problems such as collision avoidance in autonomous driving.
Ziwei Wang, Timothy Molloy, Pieter van Goor and Robert Mahony
If you use or discuss our event blob tracking method, please cite our paper as follows:
@Article{Wang_2023_Event,
author = {Wang, Ziwei and Molloy, Timothy and van Goor, Pieter and Mahony, Robert},
title = {Event Blob Tracking: An Asynchronous Real-Time Algorithm},
journal = arxiv,
year = 2023,
month = July,
url = {https://arxiv.org/abs/2307.10593},
arxivid = {2307.10593}
}
Should you have any questions or suggestions, please don't hesitate to get in touch with ziwei.wang1@anu.edu.au