A New View-Calibrated Approach for Abnormal Gait Detection

  • Kuo-Wei Lin
  • Shu-Ting Wang
  • Pau-Choo Chung
  • Ching-Fang Yang
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 21)

Abstract

Gait, or the style of walking, has been recently a popular topic in vision-based analysis. Most vision-based works about gait are devoted to the application of human recognition, but abnormal walking styles are rarely discussed. Accordingly, a vision-based method is proposed to analyze abnormal types of walking. In the proposed method, a background subtraction algorithm is applied to segment out the silhouette of the walker at each frame in a sequence. For each frame, we define a feature based on the length between two legs and the height of the individual, called aspect ratio (AR). By observing this feature value across time (or frame), a periodic wave is obtained. With this analysis, a few abnormal types of walking can be distinguished. Since an oblique camera view angle causes a distortion of the AR wave, a rectification mechanism is proposed based on a camera pinhole model to reduce the view angle effect. The experimental results show that the proposed rectification method identified abnormal walking patterns reliably irrespective of the direction in which the individual walks relative to the camera plane.

Keywords

aspect ratio abnormal gait AR rectification 

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References

  1. 1.
    Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 27(2) (February 2005) Google Scholar
  2. 2.
    Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette Analysis-Based Gait Recognition for Human Identification. IEEE Trans. Pattern Analysis and Machine Intelligence 25(12), 1505–1518 (2003) Google Scholar
  3. 3.
    BenAbdelkader, C., Culter, R., Davis, L.: Stride and Cadence as a Bio-metric in Automatic Person Identification and Verification. In: Proc. Int. Conf. Automatic Face and Gesture Recognition (2002) Google Scholar
  4. 4.
    Cutler, R., BenAbdelkader, C., Davis, L.S.: Motion based recognition of people in eigengait space. In: Proc. IEEE Conf. Face and Gesture Recognition, pp. 267–272 (2002) Google Scholar
  5. 5.
    Lee, L., Grimson, W.E.L.: Gait analysis for recognition and classification. In: Proc. IEEE Conf. Face and Gesture Recognition, pp. 155–161 (2002) Google Scholar
  6. 6.
    Cunado, D., Nash, J.M., Nixon, M.S., Carter, J.N.: Gait extraction and de-scription by evidence-gathering. In: Proc. Int. Conf. Audio and Video Based Biometric Person Authentication, pp. 43–48 (1995) Google Scholar
  7. 7.
    Bauckhage, C., Tsotsos, J.K., Bunn, F.E.: Automatic detection of abnormal gait. Image and Vision Computing 27(1-2), 108–115 (2009) Google Scholar
  8. 8.
    Lin, K.W., Chung, P.C.: Vision-Based Gait Analysis. M. S. thesis, Institute of Computer and Communication, Cheng Kung University (2005) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kuo-Wei Lin
    • 1
  • Shu-Ting Wang
    • 1
  • Pau-Choo Chung
    • 1
  • Ching-Fang Yang
    • 1
  1. 1.Dept. of Electrical Engineering, Institute of Computer and Communication EngineeringNational Cheng Kung UniversityTainanTaiwan ROC

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