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

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 × 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 user’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 10 ⁻⁵ within the processing time window of 0.67 s and a success probability of 100 % 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 × 10 ⁻³, over a transmission distance of 1.17 m.