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Video-Based Air Quality Measurement With Dual-Channel 3-D Convolutional Network by Zhenyu Wang; Shaolong Yue; Chunfeng Song is a Computer Science article available to read on EtoBox.

What is Video-Based Air Quality Measurement With Dual-Channel 3-D Convolutional Network about?

Air pollution detection and measurement is an important problem. Fast and effective explanation of the air quality is a necessary technique for monitoring the air pollution. However, existing air quality measurement devices severely rely on many sensors, which are not only expensive, but also inconvenient to carry. With the development of deep learning technology, computer vision based task such as the video processing and understanding have achieved great progress. Recently, image based air quality measuring methods have been proposed and achieved satisfying accuracy in specific scenes. Whereas the performance of those methods are not stable due to the noises in images and missed temporal relations between single images. With the rising of short video platform, the acquisition and dissemination of video data becomes more convenient. To address the problems of image based air pollution measurement, we propose a video based dual-channel 3D convolution network for stable and accurate measuring. Besides the basic visual channel, we add a semantic channel to guide the network learn region-level features. The features from two channels are combined for stable prediction. Moreover, to ev

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Author

Zhenyu Wang; Shaolong Yue; Chunfeng Song

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)