Asynchronous Blob Tracker for Event Cameras
This work addresses the need for reliable object tracking in dynamic environments, such as for collision avoidance in autonomous driving, but it appears incremental as it builds on existing methods like nearest neighbor classifiers and Kalman filters.
The paper tackles the problem of tracking fast-moving objects with event cameras by proposing a novel algorithm for asynchronous real-time tracking of event blobs, achieving highly accurate blob tracking, velocity estimation, and shape estimation even under challenging conditions like high-speed motions over 11000 pixels/s.
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.