Machine Learning

End-to-End United Video Dehazing and Detection

Tagged: , ,

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 1 week, 3 days ago.


  • arXiv
    5 pts

    End-to-End United Video Dehazing and Detection

    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.

    End-to-End United Video Dehazing and Detection
    by Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, Dan Feng
    https://arxiv.org/pdf/1709.03919v1.pdf

You must be logged in to reply to this topic.