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arXiv · 1709.03919

End-to-End United Video Dehazing and Detection

Boyi Li·Xiulian Peng·Zhangyang Wang·Jizheng Xu·Dan Feng
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Abstract

The recent development of CNN-based image dehazing has revealed the effectiveness of end-to-end modeling. However, extending the idea to end-to-end video dehazing has not been explored yet. In this paper, we propose an End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal consistency between consecutive video frames. A thorough study has been conducted over a number of structure options, to identify the best temporal fusion strategy. Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model. The resulting augmented end-to-end pipeline has demonstrated much more stable and accurate detection results in hazy video.

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Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, Dan Feng. 2017-09-12. End-to-End United Video Dehazing and Detection. https://arxiv.org/abs/1709.03919

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