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A Multi-Scale Recurrent Framework for Motion Segmentation With Event Camera
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DOI：10.1109/ACCESS.2023.3299597.png)
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摘要
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Motion segmentation is a formidable computer vision task, aiming to segment moving targets from a dynamic scene. In this paper, we choose to introduce an additional modality to bolster the robustness. The event camera is a bio-inspired sensor that accurately detects and captures intensity changes with exceptional temporal resolution and dynamic range, which is an optimal choice for motion segmentation. Therefore, we present a novel framework for event-based motion segmentation and propose Multi-Scale Recurrent Neural Network (MSRNN) to fuse temporal information efficiently. To our best knowledge, it is the first time that a multi-scale recurrent architecture is implemented in event-based motion segmentation. The proposed framework is evaluated through experiments conducted on the EV-IMO dataset. Our method achieves a mean Intersection-over-Union (mIoU) of 82.0%, which sets a new state-of-the-art in motion segmentation. To further validate our approach in arduous real-world scenarios, we introduce the Event Challenging Motion dataset, consisting of 350 images and corresponding events, in which our method outperforms the other methods by 1.5% in Intersection-over-Union (IoU).
Keyword:
Motion segmentation
event cameras
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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