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A Filtering Algorithm for Marine Environmental Time Series Monitoring Data Based on Kinect Depth Information

ABSTRACT Wei, M. and Ma, G., 2020. A filtering algorithm for marine environmental time series monitoring data based on Kinect depth information. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 762–765. Coconut Creek (Florida), ISSN 0749-0208. In order to reduce the running time and computational cost of data filtering algorithm, a data filtering algorithm for marine environmental time series monitoring based on Kinect depth information was put forward. Firstly, the depth information was collected through the imaging principle of Kinect device. Secondly, the weighted mean algorithm was used to improve the stability of depth images by continuous multi-frame depth images in time. On this basis, the corresponding color image was used as a guide to fill the holes in the depth image according to the constraints of color consistency. Finally, the classical median filtering algorithm was used to smooth the depth image and remove noise. Thus, the time series monitoring data of marine environment based on Kinect depth information was filtered. Experimental results show that the proposed algorithm can effectively reduce the running time and computational overhead.