Event Camera Object Detection at Arbitrary Frequencies
Keywords: Event Camera Object Detection, Low-Latency Vision, Label-Efficient Learning
TL;DR: FlexEvent is a novel event-based object detector capable of handling arbitrary detection frequencies at challenging conditions.
Abstract: Event cameras offer unparalleled advantages for real-time perception in dynamic environments, thanks to their microsecond-level temporal resolution and asynchronous operation. However, existing event-based object detection methods are limited by fixed-frequency paradigms, which fail to fully exploit the high-temporal resolution and adaptability of event cameras. To address these limitations, we propose FlexEvent, a novel event camera object detection framework that enables detection at arbitrary frequencies. FlexEvent consists of two key components: FlexFuser, an adaptive event-frame fusion module that integrates high-frequency event data with rich semantic information from RGB frames, and FAL, a frequency-adaptive learning mechanism that generates frequency-adjusted labels to enhance model generalization across varying operational frequencies. This combination allows FlexEvent to detect objects with high accuracy in both fast-moving and static scenarios, while adapting to dynamic environments. Extensive experiments on large-scale event camera datasets demonstrate that our approach surpasses state-of-the-art methods, achieving significant improvements in both standard and high-frequency settings. Notably, FlexEvent maintains robust performance when scaling from 20 Hz to 90 Hz and delivers accurate detection up to 180 Hz, proving its effectiveness in extreme conditions. Our framework sets a new benchmark for event-based object detection and paves the way for more adaptable, real-time vision systems. The code will be made publicly available to facilitate future research.
Supplementary Material: zip
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 589
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