한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제21권12호
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  • Pages.1473-1480
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  • 2018
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
권호 표지
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Human Action Recognition Based on An Improved Combined Feature Representation

  • Zhang, Ning (Dept. of Information Communication Engineering, Tongmyong University) ;
  • Lee, Eung-Joo (Dept. of Information Communication Engineering, Tongmyong University)
  • 투고 : 2018.10.08
  • 심사 : 2018.11.01
  • 발행 : 2018.12.31
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초록

The extraction and recognition of human motion characteristics need to combine biometrics to determine and judge human behavior in the movement and distinguish individual identities. The so-called biometric technology, the specific operation is the use of the body's inherent biological characteristics of individual identity authentication, the most noteworthy feature is the invariance and uniqueness. In the past, the behavior recognition technology based on the single characteristic was too restrictive, in this paper, we proposed a mixed feature which combined global silhouette feature and local optical flow feature, and this combined representation was used for human action recognition. And we will use the KTH database to train and test the recognition system. Experiments have been very desirable results.

키워드

MTMDCW_2018_v21n12_1473_f0001.png 이미지

Fig. 1. Background subtraction method.

MTMDCW_2018_v21n12_1473_f0002.png 이미지

Fig. 2. Contour vector results.

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Fig. 3. Light flow diagram.

MTMDCW_2018_v21n12_1473_f0004.png 이미지

Fig. 4. Optical flow feature extraction.

MTMDCW_2018_v21n12_1473_f0005.png 이미지

Fig. 5. KTH database six action diagram.

Table 1. Select the recognition rate corresponding to the different characteristics

MTMDCW_2018_v21n12_1473_t0001.png 이미지

Table 2. The different features combine the corresponding recognition rates

MTMDCW_2018_v21n12_1473_t0002.png 이미지

참고문헌

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피인용 문헌

  1. Human Motion Recognition Based on Spatio-temporal Convolutional Neural Network vol.23, pp.8, 2018, https://doi.org/10.9717/kmms.2020.23.8.977