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Photographing System Employing a Shoulder-Mounted PTZ Camera for Capturing the Composition Designated by the User’s Hand Gesture

  • Shunsuke Sugasawara
  • Yasuyuki Kono
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10714)

Abstract

We have developed the wearable system for photographing of the scenery/composition designated by the user’s hand frame with a shoulder-mounted camera. The hand frame is the gesture of making a rectangular enclosure with both hands when the user considers the composition of a picture. The system detects the hand region of the user from an image of a head-mounted camera, and gets a “picking region image” by recognizing the hand gesture. The picking region is the region in the hand frame indicated by the user through the image of the head-mounted camera. It photographs high resolution image of the similar composition as the picking region image, called “target region image” by controlling PTZ (pan/tilt/zoom) of the shoulder-mounted camera. It performs robust control on noise such as the user’s body sway.

Keywords

Shoulder-mounted camera PTZ Feature point tracking Wearable system 

References

  1. 1.
    Fuchi, K., Takahashi, S., Tanaka, Z.: A system for taking a picture by hand gesture. In: Proceedings of the 70th National Convention of IPSJ, Japan (2008)Google Scholar
  2. 2.
    Chu, S., Tanaka, J.: Hand gesture for taking self portrait. In: Jacko, J.A. (ed.) HCI 2011. LNCS, vol. 6762, pp. 238–247. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21605-3_27 CrossRefGoogle Scholar
  3. 3.
    Sakata, N., Kurata, T., Kourogi, M., Kuzuoka, H., Billinghurst, M.: Remote collaboration using a shoulder-worn active Camera/Laser. In: Multimedia, Distributed, Cooperative, and Mobile Symposium (Dicomo 2004), Japan, pp. 377–380 (2004)Google Scholar
  4. 4.
    Song, J., Sörös, G., Pece, F., Fanello, S.R., Izadi, S., Keskin, C., Hilliges, O.: In-air gestures around unmodified mobile devices. In: ACM User Interface Software and Technology Symposium (UIST 2014), USA, pp. 319–329 (2014)Google Scholar
  5. 5.
    Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 183–196. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15552-9_14 CrossRefGoogle Scholar
  6. 6.
    Bradski, G., Kaehler, A.: Learning OpenCV - Computer Vision with the OpenCV Library. O’Reilly Media, USA (2008)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Graduate School of Science and TechnologyKwansei Gakuin UniversitySandaJapan

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