Asynchronous Blob Tracker for Event Cameras

TL;DR

This paper introduces a real-time asynchronous algorithm for tracking event blobs in event cameras, enabling high-speed, accurate object tracking and velocity estimation under challenging conditions, with potential applications in autonomous driving.

Contribution

It presents a novel asynchronous tracking algorithm using event data, combining a nearest neighbor classifier and Kalman filter for improved accuracy and speed.

Findings

Achieves high-precision blob tracking at speeds over 11000 pixels/sec.

Provides accurate velocity and shape estimation under challenging lighting.

Enables secondary applications like time-to-contact estimation.

Abstract

Event-based cameras are 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 such as car headlights or any quickly moving foreground objects generate event blob data. The proposed algorithm uses a nearest neighbour classifier with a dynamic threshold criteria for data association coupled with an extended Kalman filter to track the event blob state. Our algorithm achieves highly accurate blob tracking, velocity estimation, and shape estimation even under challenging lighting conditions and high-speed motions (> 11000 pixels/s). 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.