FE-DeTr: Keypoint Detection and Tracking in Low-quality Image Frames with Events
arxiv(2024)
摘要
Keypoint detection and tracking in traditional image frames are often
compromised by image quality issues such as motion blur and extreme lighting
conditions. Event cameras offer potential solutions to these challenges by
virtue of their high temporal resolution and high dynamic range. However, they
have limited performance in practical applications due to their inherent noise
in event data. This paper advocates fusing the complementary information from
image frames and event streams to achieve more robust keypoint detection and
tracking. Specifically, we propose a novel keypoint detection network that
fuses the textural and structural information from image frames with the
high-temporal-resolution motion information from event streams, namely FE-DeTr.
The network leverages a temporal response consistency for supervision, ensuring
stable and efficient keypoint detection. Moreover, we use a spatio-temporal
nearest-neighbor search strategy for robust keypoint tracking. Extensive
experiments are conducted on a new dataset featuring both image frames and
event data captured under extreme conditions. The experimental results confirm
the superior performance of our method over both existing frame-based and
event-based methods.
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