Performance evaluation of neural network assisted motion detection schemes implemented within indoor optical camera based communications

Teli, Shivani Rajendra, Zvanovec, Stanislav and Ghassemlooy, Zabih (2019) Performance evaluation of neural network assisted motion detection schemes implemented within indoor optical camera based communications. Optics Express, 27 (17). pp. 24082-24092. ISSN 1094-4087

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Official URL: https://doi.org/10.1364/OE.27.024082

Abstract

This paper investigates the performance of the neural network (NN) assisted motion detection (MD) over an indoor optical camera communication (OCC) link. The proposed study is based on the performance evaluation of various NN training algorithms, which provide efficient and reliable MD functionality along with vision, illumination, data communications and sensing in indoor OCC. To evaluate the proposed scheme, we have carried out an experimental investigation of a static indoor downlink OCC link employing a mobile phone front camera as the receiver and an 8 x000D7; 8 red, green and blue light-emitting diodes array as the transmitter. In addition to data transmission, MD is achieved using a camera to observe userx02019;s finger movement in the form of centroids via the OCC link. The captured motion is applied to the NN and is evaluated for a number of MD schemes. The results show that, resilient backpropagation based NN offers the fastest convergence with a minimum error of 10x02212;5 within the processing time window of 0.67 s and a success probability of 100 x00025; for MD compared to other algorithms. We demonstrate that, the proposed system with motion offers a bit error rate which is below the forward error correction limit of 3.8 x000D7; 10x02212;3, over a transmission distance of 1.17 m.

Item Type: Article
Uncontrolled Keywords: CMOS cameras, Green light emitting diodes, High speed photography, Image processing, Optical wireless communication, Visible light
Subjects: F200 Materials Science
F300 Physics
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 08 Aug 2019 10:04
Last Modified: 19 Nov 2019 10:16
URI: http://nrl.northumbria.ac.uk/id/eprint/40275

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