Asynchronous Blob Tracker for Event Cameras

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.

18 pages, 16 figures. The manuscript was accepted on August 7, 2024, by IEEE Transactions on Robotics

References in corpus (7)

• Event-based Vision: A Survey
• The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM
• Event-based Moving Object Detection and Tracking
• End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking
• Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking
• An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras
• Event-based Star Tracking via Multiresolution Progressive Hough Transforms

Cited by in corpus (2)

• ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras
• Asynchronous Multi-Object Tracking with an Event Camera