Traffic sign recognition using visual attribute learning and convolutional neural network

Qian, Rong-Qiang, Yue, Yong, Coenen, Frans and Zhang, Bai-Ling
(2016) Traffic sign recognition using visual attribute learning and convolutional neural network. In: 2016 International Conference on Machine Learning and Cybernetics (ICMLC), 2016-7-10 - 2016-7-13.

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The problem of extracting high level information from digital images and videos is frequently faced in the area of computer vision and machine learning. For the recognition of traffic signs, a lot of outstanding methods have been proposed, and deep models demonstrates that their powerful representation capacity, can archieve dominant performances. In this paper a method for recognizing traffic signs is proposed founded on a novel visual attribute mechanisms; whereby attributes are generated using Convolutional Neural Networks (CNN). In comparison with previous methods founded on the use of CNN for feature extractor and Multi-Layer Perception (MLP) as classifier, the Max Pooling Positions (MPPs) proposed in this paper predict visual attributes that provide a useful linkage between low-level features and high-level sematic tasks. The results show that outstanding performances can be achieved using MPPs.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: 46 Information and Computing Sciences, 4611 Machine Learning, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence
Depositing User: Symplectic Admin
Date Deposited: 19 Jan 2018 09:21
Last Modified: 20 Jun 2024 19:11
DOI: 10.1109/icmlc.2016.7860932
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