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Time-segmentation and position-free recognition of air-drawn gestures and characters in videos

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Abstract

We report the recognition in video streams of isolated alphabetic characters and connected cursive textual characters, such as alphabetic, hiragana and kanji characters, that are drawn in the air. This topic involves a number of difficult problems in computer vision, such as the segmentation and recognition of complex motion on videos. We use an algorithm called time–space continuous dynamic programming (TSCDP), which can realize both time- and location-free (spotting) recognition. Spotting means that the prior segmentation of input video is not required. Each reference (model) character is represented by a single stroke that is composed of pixels. We conducted two experiments involving the recognition of 26 isolated alphabetic characters and 23 Japanese hiragana and kanji air-drawn characters. We also conducted gesture recognition experiments based on TSCDP, which showed that TSCDP was free from many of the restrictions imposed by conventional methods.

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References

  1. Alon J, Spatiotemporal gesture segmentation. Dissertation for Doctor of Philosophy Boston University (2006)

  2. Baumann F, Liao J, Ehlers A, Rosenhahn B (2014) Motion binary patterns for action recognition, in 3rd International conference on pattern recognition applications and methods

  3. Chen F, Fu C, Huang C (2003) Hand gesture recognition using a real-time tracking method and hidden markov models. Image and Video Computing 21(8):745

    Article  Google Scholar 

  4. Ezaki N, Sugimoto M, Kiyota K, Yamamoto S (2010) Character recognition by using acceleration sensor: proposing a character input method using Wiimote, meeting on image recognition and understanding IS2-48, 1094

  5. Gao W, Ma J, Wu J, Wang C (2000) Sign language recognition based on HMM/ANN/DP. Int J Pattern Recognit Artif Intell 14(5):587

    Article  Google Scholar 

  6. Horo T, Inaba M (2006) A Handwriting Recognition System Using Multiple Cameras, Workshop on Interactive Systems and Software (WISS2006)

  7. Kolsch M, Turk M (2004) In Proceedings Robust hand detection, Sixth IEEE International conference on automatic face and gesture recognition pp 614–619

  8. Laptev I (2004) Local Spatio-Temporal Image Features for Motion Interpretation, Local spatio-temporal image features for motion interpretation. Ph.D. thesis, Computational Vision and Active Perception laboratory (CVAP), NADA, KTH, Stockholm

  9. Nakai M, Yonezawa H (2009) Aerial handwritten character recognition using an acceleration sensor, forum on information technology H-19 p133

  10. Oka R (1998) Spotting method for classification of real world data. Comput J 41(8):559

    Article  MATH  Google Scholar 

  11. Oka R, Matsuzaki T (2012) Robustness for time-spatial deformation of an occlusion realized in time-space continuous dynamic programming. Joint Technical Meeting on Information Processing and Innovative Industrial Systems 27(6):873

    Google Scholar 

  12. Okada T, Muraoka Y (2003) Letter input system for handwriting gestures, transactions of the institute of electronics. Information an Communication Engineers D-II J86-D-II 7:1015

    Google Scholar 

  13. Ong SCW, Ranganath S (2005) Automatic sign language analysis: A survey and the future beyond lexical meaning. IEEE Trans Pattern Anal Mach Intell 27(6):873

    Article  Google Scholar 

  14. Sato A, Shinoda K, Furui S (2010) Sign language recognition using time-of-flight camera, meeting on image recognition and understanding IS3-44

  15. Sclaro S, Betke M, Kollios G, Alon J, Athitsos V, Li R, Magee J, Tian TP (2005) Tracking analysis and recognition of human gestures in video, ICDAR: International Conference on document analysis and recognition

  16. Yang M, Ahuja N, Tabb M (2002) Extraction of 2D motion trajectories and its application to hand gesture recognition. IEEE Trans Pattern Anal Mach Intell 24(8):1061

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Turuga Ikkichimachi, Aizuwakamatsu-city, Fukushima, Japan

    Yuki Niitsuma, Syunpei Torii, Yuichi Yaguchi & Ryuichi Oka

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  1. Yuki Niitsuma
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  2. Syunpei Torii
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  3. Yuichi Yaguchi
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  4. Ryuichi Oka
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Correspondence to Ryuichi Oka.

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Niitsuma, Y., Torii, S., Yaguchi, Y. et al. Time-segmentation and position-free recognition of air-drawn gestures and characters in videos. Multimed Tools Appl 75, 11615–11639 (2016). https://doi.org/10.1007/s11042-015-2669-3

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  • DOI: https://doi.org/10.1007/s11042-015-2669-3

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