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Development of Unmanned Video Recording System using Mobile

모바일을 이용한 무인 영상 녹화 시스템 개발

  • Ahn, Byeongtae (Department of Computer, Liberal & Arts College, Anyang University)
  • 안병태 (안양대학교 교양대학 컴퓨터전공)
  • Received : 2019.03.14
  • Accepted : 2019.06.07
  • Published : 2019.06.30

Abstract

Recently, a self-camera that generates and distributes a large amount of moving images has been rapidly increasing due to the appearance of SNS such as Facebook, Instagram, and Tweet using mobile. In particular, the amount of SNS connections using mobile phones is significantly increasing in terms of usage, number of connections, and usage time. However, the use of a self-recording system using a smartphone by itself is extremely limited not only in terms of usage but also in frequency of use. In addition, the conventional unattended recording system is a very expensive system that automatically records and tracks an object to be photographed using an infrared signal. Therefore, this paper developed a low cost unmanned recording system using mobile phone. The system consists of a commercial mobile camera, a servomotor for moving the camera from side to side, a microcontroller for controlling the motor, and a commercial wireless Bluetooth earset for video audio input. And it is an unmanned automation system using mobile, and anyone can record image by self image tracking.

최근, 모바일을 이용한 페이스북, 인스타그램, 트윗과 같은 SNS의 등장으로 대량의 동영상을 제작 및 배포하는 셀프 카메라가 급속도로 증가하고 있다. 특히, 모바일 폰을 이용한 SNS 접속량은 기존 PC보다 사용량, 접속 횟수, 사용시간에서 대폭적으로 증가하고 있다. 그러나 스스로 스마트 폰을 이용한 셀프 녹화 시스템 사용은 사용 방법에 있어서 뿐만 아니라 사용 빈도에서도 극히 제한적이다. 그리고 기존의 무인 녹화 시스템은 적외선 신호를 이용하여 촬영 대상을 자동으로 추적 및 회전하여 녹화하는 시스템으로 매우 고가이다. 따라서 본 논문에서는 모바일 폰을 이용한 저비용 무인녹화 시스템을 개발하였다. 본 시스템은 상용 모바일 카메라와 좌우로 카메라를 움직이는 서보모터, 모터를 제어하는 마이크로 컨트롤러 그리고 동영상 오디오 입력을 담당할 상용 무선 블루투스 이어셋으로 구성된다. 그리고 모바일을 이용한 무인 자동화 시스템으로 누구나 개인 스스로 이미지 영상 추적에 따른 녹화 기능이 제공된다.

Keywords

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Fig. 1. TRI(Tracking & Recording Itself)

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Fig. 2. ‘PTZ Camera’(Model : B00DPSBV1G)

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Fig. 3. Implemented Face Detection Screen

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Fig. 4. Implemented CAM-Shift Screen

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Fig. 5. Target Tracking Algorithm Flowchart

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Fig. 6. Decision criterion for rotation direction of pan-tilt

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Fig. 7. Added Gestures Case of CAM-Shift

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Fig. 8. UI Structure

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Fig. 9. Mobile App Main Screen

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Fig. 10. Compare Table of Swivl, PTZ Camera, TRI

References

  1. Yu-Jie He, Min Li, JinLi Zhang, and Jun-Ping Yao, "Infrared target tracking via weighted correlation filter", Infrared Physics & Technology, Volume 73, November 2015, Pages 103-114 https://doi.org/10.1016/j.infrared.2015.09.010
  2. Davide M. Raimondo, S. Gasparella, D. Sturzenegger, J. Lygeros, and M. Morari, "A tracking algorithm for PTZ cameras", IFAC Proceedings Volumes, Volume 43, Issue 19, 2010, Pages 61-66 https://doi.org/10.3182/20100913-2-fr-4014.00060
  3. Peng Zhang, Tao Zhuo, Lei Xie, and Yanning Zhang, "Deformable object tracking with spatiotemporal segmentation in big vision surveillance", Neurocomputing, Volume 204, 5 September 2016, Pages 87-96 https://doi.org/10.1016/j.neucom.2015.07.149
  4. Shinsuke Yasukawa, Hirotsugu Okuno, Kazuo Ishii, and Tetsuya Yagi, "Real-time object tracking based on scale-invariant features employing bio-inspired hardware", Neural Networks, Volume 81, September 2016, Pages 29-38 https://doi.org/10.1016/j.neunet.2016.05.002
  5. Gerda Edelman, and Jurrien Bijhold, "Tracking people and cars using 3D modeling and CCTV", Forensic Science International, Volume 202, Issues 1-3, 10 October 2010, Pages 26-35 https://doi.org/10.1016/j.forsciint.2010.04.021
  6. Jigang Liu, Dongquan Liu, Justin Dauwels, and Hock Soon Seah, "3D Human motion tracking by exemplar-based conditional particle filter", Signal Processing, Volume 110, May 2015, Pages 164-177 https://doi.org/10.1016/j.sigpro.2014.08.028
  7. Jing Wang, Yanyu Lu, Liujun Gu, Chuanqing Zhou, and Xinyu Chai, "Moving object recognition under simulated prosthetic vision using background-subtraction-based image processing strategies", Information Sciences, Volume 277, 1 September 2014, Pages 512-524 https://doi.org/10.1016/j.ins.2014.02.136
  8. Jihao Yin, Chongyang Fu, and Jiankun Hu, "Using incremental subspace and contour template for object tracking", Journal of Network and Computer Applications, Volume 35, Issue 6, November 2012, Pages 1740-1748 https://doi.org/10.1016/j.jnca.2012.06.005
  9. Fayao Liu, Chunhua Shen, Ian Reid, and Anton van den Hengel, "Online unsupervised feature learning for visual tracking", Image and Vision Computing, Volume 51, July 2016, Pages 84-94 https://doi.org/10.1016/j.imavis.2016.04.008
  10. Xuewei Shen, Xiubao Sui, Kechen Pan, and Yuanrong Tao, "Adaptive pedestrian tracking via patch-based features and spatial-temporal similarity measurement", Pattern Recognition, Volume 53, May 2016, Pages 163-173 https://doi.org/10.1016/j.patcog.2015.11.017
  11. Shuo Chen, and Chengjun Liu, ""Eye detection using discriminatory Haar features and a new efficient SVM", Image and Vision Computing, Volume 33, January 2015, Pages 68-77 https://doi.org/10.1016/j.imavis.2014.10.007
  12. Haichao Zheng, Xia Mao, Lijiang Chen, and Xiaogeng Liang, "Adaptive edge-based mean shift for drastic change gray target tracking", Optik - International Journal for Light and Electron Optics, Volume 126, Issue 23, December 2015, Pages 3859-3867 https://doi.org/10.1016/j.ijleo.2015.07.160
  13. M. Gentili, R. Sannino, and M. Petracca, "BlueVoice: Voice communications over Bluetooth Low Energy in the Internet of Things scenario", Computer Communications, Volumes 89-90, 1 September 2016, Pages 51-59 https://doi.org/10.1016/j.comcom.2016.03.004
  14. Satrughan Kumar, and Jigyendra Sen Yadav, "Video object extraction and its tracking using background subtraction in complex environments", Perspectives in Science, In Press, Corrected Proof, Available online 26 April 2016
  15. Ruolin Zhang, and Jian Ding, "Object Tracking and Detecting Based on Adaptive Background Subtraction", Procedia Engineering, Volume 29, 2012, Pages 1351-1355 https://doi.org/10.1016/j.proeng.2012.01.139
  16. Fayez F.M. El-Sousy, "Intelligent mixed H2/$H{\infty}$ adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system", Applied Soft Computing, Volume 41, April 2016, Pages 22-50 https://doi.org/10.1016/j.asoc.2015.12.009
  17. Mark David Jenkins, Peter Barrie, Tom Buggy, and Gordon Morison, "Extended fast compressive tracking with weighted multi-frame template matching for fast motion tracking", Pattern Recognition Letters, Volume 69, 1 January 2016, Pages 82-87 https://doi.org/10.1016/j.patrec.2015.10.014
  18. Sandeep Singh Sengar, and Susanta Mukhopadhyay, "Moving object area detection using normalized self adaptive optical flow", Optik - International Journal for Light and Electron Optics, Volume 127, Issue 16, August 2016, Pages 6258-6267 https://doi.org/10.1016/j.ijleo.2016.03.061
  19. Wei-Chuan Zhang, and Peng-Lang Shui, "Contour-based corner detection via angle difference of principal directions of anisotropic Gaussian directional derivatives", Pattern Recognition, Volume 48, Issue 9, September 2015, Pages 2785-2797 https://doi.org/10.1016/j.patcog.2015.03.021
  20. Shyh Wei Teng, Rafi Md. Najmus Sadat, and Guojun Lu, "Effective and efficient contour-based corner detectors", Effective and efficient contour-based corner detectors https://doi.org/10.1016/j.patcog.2015.01.016