Let There Be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer

نویسندگان

چکیده

In recent years, image and video surveillance have made considerable progresses to the Intelligent Transportation Systems (ITS) with help of deep Convolutional Neural Networks (CNNs). As one state-of-the-art perception approaches, detecting interested objects in each frame is widely desired by ITS. Currently, object detection shows remarkable efficiency reliability standard scenarios such as daytime scenes favorable illumination conditions. However, face adverse conditions nighttime, loses its accuracy significantly. One main causes problem lack sufficient annotated datasets nighttime scenes. this paper, we propose a framework alleviate decline when taken using translation method. We utilize style based StyleMix method acquire pairs day time training data for following translation. To detail corruptions caused Generative Adversarial (GANs), Kernel Prediction Network (KPN) refine The KPN network trained task together adapt model vehicle directly. Experiments on verified effectiveness proposed approach.

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ژورنال

عنوان ژورنال: IEEE Transactions on Circuits and Systems for Video Technology

سال: 2022

ISSN: ['1051-8215', '1558-2205']

DOI: https://doi.org/10.1109/tcsvt.2021.3081999