Trace Projection Transformation: a new method for measurement of debris flow surface velocity fields

Yan YAN, Peng CUI, Xiaojun GUO, Yonggang GE

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PDF(3321 KB)
Front. Earth Sci. ›› 2016, Vol. 10 ›› Issue (4) : 761-771. DOI: 10.1007/s11707-015-0576-6
RESEARCH ARTICLE
RESEARCH ARTICLE

Trace Projection Transformation: a new method for measurement of debris flow surface velocity fields

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Abstract

Spatiotemporal variation of velocity is important for debris flow dynamics. This paper presents a new method, the trace projection transformation, for accurate, non-contact measurement of a debris-flow surface velocity field based on a combination of dense optical flow and perspective projection transformation. The algorithm for interpreting and processing is implemented in C++ and realized in Visual Studio 2012. The method allows quantitative analysis of flow motion through videos from various angles (camera positioned at the opposite direction of fluid motion). It yields the spatiotemporal distribution of surface velocity field at pixel level and thus provides a quantitative description of the surface processes. The trace projection transformation is superior to conventional measurement methods in that it obtains the full surface velocity field by computing the optical flow of all pixels. The result achieves a 90% accuracy of when comparing with the observed values. As a case study, the method is applied to the quantitative analysis of surface velocity field of a specific debris flow.

Keywords

debris flow / surface velocity field / spatiotemporal variation / dense optical flow / perspective projection transformation

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Yan YAN, Peng CUI, Xiaojun GUO, Yonggang GE. Trace Projection Transformation: a new method for measurement of debris flow surface velocity fields. Front. Earth Sci., 2016, 10(4): 761‒771 https://doi.org/10.1007/s11707-015-0576-6

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Acknowledgments

This study was financially supported by the Key Technologies R&D Program of Sichuan Province (2014SZ0163), the External Cooperation Program of BIC, CAS (131551KYSB20130003), the Open Research Fund by Sichuan Engineering Research Center for Emergency Mapping & Disaster Reduction (K2015B011) and the Key Technologies R&D Program of Sichuan Province (2015SZ0046). The authors thank the DDFORS for providing experimental facilities and data.

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2015 Higher Education Press and Springer-Verlag Berlin Heidelberg
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