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


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

July 7, 2016
November 19, 2016
Online released:
May 19, 2017
May 20, 2017
weather recognition, deep learning, semantic features, sparse decomposition
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.
Cite this article as:
W. Liu, Y. Yang, and L. Wei, “Weather Recognition of Street Scene Based on Sparse Deep Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.3, pp. 403-408, 2017.
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Last updated on Jul. 12, 2024