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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 9/2019

19.11.2018 | Original Article

Depth estimation from infrared video using local-feature-flow neural network

verfasst von: Shouchuan Wu, Haitao Zhao, Shaoyuan Sun

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 9/2019

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Abstract

Depth estimation is essential for infrared video processing. In this paper, a novel depth estimation method, called local-feature-flow neural network (LFFNN), is proposed for generating depth maps for each frame of an infrared video. LFFNN extracts local features of a frame with the addition of inter-frame features, which is extracted from the previous frames on the corresponding region in the infrared video. LFFNN is designed for extracting the local features flow in the infrared video, learning better depth-related features through three control gates by inter-frame features propagation as the video progresses. After feature extraction, a pixel-level classifier is created to estimate depth level of different pixels in the infrared video. Our proposed approach achieves state-of-the-art depth estimation performances on the test dataset.
Vorheriger Artikel Digital hardware realization of a novel adaptive ink drop spread operator and its application in modeling and classification and on-chip training
Nächster Artikel An -norm loss based twin support vector regression and its geometric extension

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Metadaten
Titel
Depth estimation from infrared video using local-feature-flow neural network
verfasst von
Shouchuan Wu
Haitao Zhao
Shaoyuan Sun
Publikationsdatum
19.11.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 9/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0891-9

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