Journal of Electrical Engineering and Technology

  • 제11권6호
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  • Pages.1857-1862
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  • 2016
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  • 1975-0102(pISSN)
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  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM

  • Kamal, Shaharyar (Dept. of Electronics and Radio Engineering, Kyung Hee University) ;
  • Jalal, Ahmad (Dept. of Computer Science and Engineering, POSTECH) ;
  • Kim, Daijin (Dept. of Computer Science and Engineering, POSTECH)
  • 투고 : 2015.09.14
  • 심사 : 2016.05.16
  • 발행 : 2016.11.01
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초록

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

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참고문헌

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