Compressed-domain Fall Incident Detection for Intelligent Homecare

  • Authors:

  • Chia-Wen Lin;Zhi-Hong Ling;Yeng-Cheng Chang;Chung J. Kuo

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan 621;Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan 621;Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan 621;Components Business Group, Delta Electronics, Inc., Taoyuan, Taiwan 333

  • Venue:
  • Journal of VLSI Signal Processing Systems
  • Year:
  • 2007

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Abstract

This paper presents a compressed-domain fall incident detection scheme for intelligent homecare applications. For object extraction, global motion parameters are estimated to distinguish local object motions from camera motions so as to obtain a rough object mask. We then perform change detection and/or background subtraction on the DC+2AC images extracted from the incoming coded bitstream to refine the object mask. Subsequently, an object clustering algorithm is used to automatically separate the individual video objects iteratively. After detecting the moving objects, compressed-domain features of each object are then extracted for identifying and locating fall incidents. Our experiments show that the proposed method can correctly detect fall incidents in real time.