Investigation on Screen Communication Location and Tracking Algorithm based on Faster R-CNN

LIU Wen-kai; WANG Hai-rui; WU Meng-long

doi:10.13756/j.gtxyj.2020.02.002

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Investigation on Screen Communication Location and Tracking Algorithm based on Faster R-CNN

- Investigation on Screen Communication Location and Tracking Algorithm based on Faster R-CNN
- Study on Optical communication Issue 2, Pages: 7-10(2020)
- 作者机构：
  
  北方工业大学信息学院
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2020.02.002
  CLC： TN929.1;TP18;TP391.41
- Published：2020，
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[1]刘文楷,王海瑞,武梦龙.基于深度学习的屏幕通信定位跟踪算法研究[J].光通信研究,2020(02):7-10+60.

LIU Wen-kai, WANG Hai-rui, WU Meng-long. Investigation on Screen Communication Location and Tracking Algorithm based on Faster R-CNN. [J]. (2):7-10(2020)
[1]刘文楷,王海瑞,武梦龙.基于深度学习的屏幕通信定位跟踪算法研究[J].光通信研究,2020(02):7-10+60. DOI： 10.13756/j.gtxyj.2020.02.002.

LIU Wen-kai, WANG Hai-rui, WU Meng-long. Investigation on Screen Communication Location and Tracking Algorithm based on Faster R-CNN. [J]. (2):7-10(2020) DOI： 10.13756/j.gtxyj.2020.02.002.

摘要

屏幕通信是以动态条码为信息载体

以可见光为媒介的近场通信方式

因其抗干扰能力强、不占用频谱资源和部署简单等特点

具有广泛的应用场景和极大的发展潜力。文章提出了一种基于深度学习方法的屏幕通信定位跟踪算法

采用快速区域卷积神经网络（Faster R-CNN）算法对信息区域进行处理

使光学摄像头在不依赖传统定位寻像图形的情况下智能定位携信区域

进而提高单帧携信量;采用卢卡斯-卡纳德（LK）光流法对抖动引入的帧间信息区域位置变化进行估计

提升了连续帧处理速率从而提升了系统通信速率。实验结果表明

Faster R-CNN算法的平均精度均值（mAP）达到了90.91%

同时引入LK光流法提升系统的处理效率

相比于仅采用Faster R-CNN的算法处理时间缩短59.5%以上

解决了终端系统处理能力瓶颈问题。

Abstract

Screen communication is a near-field communication mode using dynamic bar code as the information carrier and visible light as the media. Due to its strong anti-interference ability

spectrum resources free

and simple deployment

it has a wide range of application scenarios and great development potential. We have proposed a screen communication location tracking algorithm based on the method of deep learning. The information area is processed by Faster Region with Convolutional Neural Networks Feature（Faster R-CNN） algorithm which makes the optical camera can intelligently locate the carrier area without relying on the traditional positioning finder pattern

resulting in the improvement of the single frame carrying. The Lucas-Kanade（LK） optical flow is used for position change estimation of the subsequent frame information region introduced by the jitter

which improves the continuous frame processing rate and the system communication rate. Experimental results show that the mean Average Precision（mAP） of the Faster R-CNN is reached up to 90.91% while the LK optical flow was introduced to improve the processing efficiency of the system

and overcome the bottleneck of the processing power of the terminal system. The processing time was shortened by 59.5% compared with the Faster R-CNN algorithm.

关键词

屏幕通信深度学习信息条码定位跟踪算法

Keywords

screen communicationdeep learninginformation bar codeposition tracking algorithm

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