@inproceedings{li2018end-to-end,
author = {Li, Boyi and Peng, Xiulian and Wang, Zhangyang and Xu, Ji-Zheng and Feng, Dan},
title = {End-to-end united video dehazing and detection},
booktitle = {Thirty-second AAAI Conference on Artificial Intelligence},
year = {2018},
month = {February},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/end-to-end-united-video-dehazing-and-detection/},
}