Moving object recognition using multi-view three-dimensional convolutional neural networks

He, Tao, Mao, Hua and Yi, Zhang (2017) Moving object recognition using multi-view three-dimensional convolutional neural networks. Neural Computing and Applications, 28 (12). pp. 3827-3835. ISSN 0941-0643

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He et al - Moving object recognition using multi-view three-dimensional convolutional neural networks AAM.pdf - Accepted Version

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Official URL: http://dx.doi.org/10.1007/s00521-016-2277-9

Abstract

Moving object recognition (MOR) is an important but challenging problem in the field of computer vision. The aim of MOR is to recognize moving objects in a given video dataset. Convolutional neural networks (CNNs) have been extensively used for image recognition and video analysis problems. Recently, a 3D-CNN, which contains 3D convolution layers, was proposed to address MOR problems by successfully extracting spatiotemporal features. In this paper, a multi-view (MV) 3D-CNN is proposed for MOR. This model combines 3D-CNNs with a well-known MV learning technique. Because multi-view learning techniques have the ability to obtain more view-related features from videos captured by different cameras, the proposed model can extract more representative features. Moreover, the model contains a special view-pooling layer that can fuse the feature information from previous layers. The proposed MV3D-CNN is applied to both real-world moving vehicle recognition and sign language recognition tasks. The experimental results show that the proposed model possesses good performance.

Item Type: Article
Uncontrolled Keywords: Moving object recognition, Multi-view learning, 3D convolutional neural networks, Feature extraction, Deep learning
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 13 Jun 2019 12:09
Last Modified: 11 Oct 2019 09:18
URI: http://nrl.northumbria.ac.uk/id/eprint/39673

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