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Asynchronous Blob Tracker for Event Cameras
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AI
DOI：10.1109/TRO.2024.3454410.png)
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摘要
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Event-based cameras are popular for tracking fast-moving objects due to their high temporal resolution, low latency, and high dynamic range. In this article, 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 neighbor 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 (> 11 000 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.
Keyword:
Asynchronous filtering
event blob
event-based camera
high dynamic range
high-speed target tracking
range estimation
real-time processing
time-to-contact (TTC)
event blob
event-based camera
high dynamic range
high-speed target tracking
range estimation
real-time processing
time-to-contact (TTC)
期刊
IF:
10.5
论文数:
3.3K
被引数:
2.8W
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引用论文
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The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM事件相机数据集和模拟器: 用于姿态估计，视觉里程计和SLAM的基于事件的数据
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