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JACIII Vol.21 No.3 pp. 403-408
doi: 10.20965/jaciii.2017.p0403
(2017)

Paper:

Weather Recognition of Street Scene Based on Sparse Deep Neural Networks

Wei Liu, Yue Yang, and Longsheng Wei

School of Automation, China University of Geosciences
Wuhan, Hubei 430074, China

Received:
July 7, 2016
Accepted:
November 19, 2016
Online released:
May 19, 2017
Published:
May 20, 2017
Keywords:
weather recognition, deep learning, semantic features, sparse decomposition
Abstract

Recognizing different weather conditions is a core component of many different applications of outdoor video analysis and computer vision. Street analysis performance, including detecting street objects, detecting road lines, recognizing street sign and etc., varies greatly with weather, so modeling based on weather recognition is the key resolution in this field. Features derived from intrinsic properties of different weather conditions contribute to successful classification. We first propose using deep learning features from convolutional neural networks (CNN) for fine recognition. In order to reduce the parameter redundancy in CNN, we used sparse decomposition to dramatically cut down the computation. Recognition results for databases show superior performance and indicate the effectiveness of extracted features.

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Last updated on Nov. 20, 2017