Xinyu Lv
ABSTRACT
The asynchronous event stream of event camera output overcomes exposure problems caused by dramatic changes in ambient light and motion blur caused by high-speed motion, which are common challenges with traditional cameras. With the increasing number of events per second delivered by event cameras, a faster feature extraction method is necessary to process large amounts of events to take advantage of event cameras for various computer vision tasks. We propose UCED-Detector, an event frame-based corner event detector that can detect features in event streams at three times the speed of the SOTA method. Firstly, we use events captured in the past to remove noise events from the current event stream. The events in the circular mask around the event to be detected are then constructed as event pairs and mark the events whose timestamps are one threshold larger than the other event in the event pair. Finally, the marked adjacent events are connected into arcs, and whether the event to be detected is a feature corner event is judged according to the arc length. To evaluate the performance of our proposed approach, we conducted extensive experiments on datasets of event cameras. The results show that our method reduces the detection time to one-third of the SOTA method and reduces the average processing time per event from 0.15 milliseconds to 0.04 milliseconds.
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Index Terms
- UCED-Detector: An Ultra-fast Corner Event Detector for Event Camera in Complex Scenes: UCED-Detector: Ultra-fast Detector An ultra-fast detector for detecting feature corner events in a high-speed event stream
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