Use of a Raspberry PI single-board computer for image acquisition and transmission

• Autorzy: Sieczkowski K. , Sondej T.
• Języki publikacji: EN

Abstrakty

EN

The article presents an analysis of the use of a single-board computer Raspberry PI for video acquisition and transmission. The article focuses on requirements necessary for the recorded image to be used for face analysis to identify facial expressions and microexpressions. The quality of the recorded video frames was verified for different resolutions and fps using PSNR (Peak Signal-to-Noise Ratio). Tests of CPU cores usage were also carried out for simultaneous recording and transmission of different types of video streams. The results show that the size of the effective image area depends on the resolution of recorded video stream. Increasing the frame rate for the given video resolution has a significant impact on the value of PSNR. And the resultant CPU usage, for the available resolutions and frame rates of the video recorded, in most cases does not exceed 15% of the total computing power of the CPU.

Słowa kluczowe

EN

Raspberry Pi; single-board computer; video monitoring; image processing

• Wydawca: Wydawnictwo PAK
• Czasopismo: Measurement Automation Monitoring
• Rocznik: 2017
• Tom: Vol. 63, No. 5
• Strony: 180--182
• Opis fizyczny: Bibliogr. 9 poz., rys., tab., wzory

Twórcy

autor

Sieczkowski K.

• Electronic Department, Military University of Technology 2 gen. Sylvester Kaliski St., 00-908 Warsaw 49, Poland

autor

Sondej T.

• Electronic Department, Military University of Technology 2 gen. Sylvester Kaliski St., 00-908 Warsaw 49, Poland

Bibliografia

• [1] Janard K., Marurngsith W.: Accelerating Realtime Face Detection on a Raspberry Pi Telepresence Robot. International Conference on the Innovative Computing Technology, pp. 136-141, 2015.
• [2] Michalak S.: Mikrokomputer Raspberry Pi jako sterownik systemu pomiarowego. Pomiary Automatyka Kontrola, vol. 60, nr 8, str. 649-651, 2014.
• [3] Pfister T., Xiaobai Li, Guoying Zhao, Pietikäinen M.: Recognising spontaneous facial micro-expressions. IEEE International Conference on Computer Vision (ICCV), pp. 1449-1456, 2011.
• [4] Prinz A.C. B., Taank V. K., Voegeli V., Walters E. L.: A novel nest-monitoring camera system using a Raspberry Pi microcomputer. Journal of Field Ornithol., vol. 87, no. 4, pp. 427-435, 2016.
• [5] Sahitya S., Lokesha H., Sudha L.K.: Real Time Application of Raspberry Pi in Compression of Images, IEEE Inter. Conf. On Recent Trends In Electr., Infor. & Comm. Tech., pp. 1047-1050, 2016.
• [6] Szabo R., Gontean A.: Industrial Robotic Automation with Raspberry PI using Image Processing. 21ST International Conference On Applied Electronics, pp. 265-268, 2016.
• [7] OV5647 Product Brief http://www.ovt.com/uploads/parts/ OV5647.pdf, 2017.
• [8] Raspberry Pi Foundation https://www.raspberrypi.org, 2017
• [9] Waveshare Electronics http://www.waveshare.com/rpi-camera-h.htm, 2017.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).