Hirokatsu KATAOKA, Ph.D.
National Institute of Advanced Industrial Science and Technology (AIST), Japan

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"Robust Human Tracking using Statistical Human Model of Appearance Variation"
Kiyoshi Hashimoto (Keio Univ), Tomoyuki Kagaya(Keio Univ), Hirokatsu Kataoka (Keio Univ), Yoshimitsu Aoki (Keio Univ)

  Human tracking in monocular image sequences has been studied in the field of computer vision for many kinds of applications such as surveillance system, intelligent room, sports video analysis and so on. Human tracking in real environment is challenging for various factors. These include illumination variation, partial or almost complete occlusion of human body, and wide variety of body shapes. In this research, we present a novel method for robust human tracking using statistical human model of appearance variation with postural change. This statistical human model can represent different postural appearances of human, and enables effective and robust human tracking with simple features such as silhouette, edge and color.

  In our proposed method, we create two statistical models to represent human-like body shape. The first is an Active Omega Model(AOM) to detect humans and track the contour of head-shoulder(Omega shape) accurately. The second is a Main-parts Link Model (MLM) to track each body parts robustly. And we track humans by applying these models.

  There are 4 steps for (i) background subtraction: MoG, (ii) human detection: AOM fitting using foreground, (iii) human tracking: 5 separated parts tracking using MLM, (iv) pose estimation: rough pose is estimated from results of separate parts tracking. We can understand human's "posture" and "simple action" as a result of this method.


References
- Kiyoshi Hashimoto, Hirokatsu Kataoka, Yuji Sato, Yoshimitsu Aoki, "Robust Human Tracking using Statistical Human Shape Model with Postual Variation", IEEE Industrial Electronics Society (IECON), Nov. 2013.

- Kiyoshi Hashimoto, Tomoyuki Kagaya, Hirokatsu Kataoka, Yuji Sato, Yoshimitsu Aoki, "Robust Human Tracking using Statistical Human Shape Model of Appearance Variation", The journal of the Institute of Image Electronics Engineers of Japan, Vol.42, No.5, pp.681-689, 2013.[PDF](in Japanese)



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