Unusual Event Detection Algorithm via Personalized Daily Activity and Vision Patterns for Single Households

  • Authors

    • Junho Ahn
    • Hwijune Park
    • Juho Jung
    • Gwang Lee
    https://doi.org/10.14419/ijet.v8i1.4.25465
  • Unusual event, Single Household, Activity, Vision, Fusion Algorithm.

  • People may be too seriously injured, incapable or endangered in emergency situations in their houses. Roommates or family members who live together in the same house can rescue or call 911 for them in the dangerous situations at home. The growth in the number of single-person households is currently rising over the decades and they have difficulties to get help from other people in case of serious injuries in their houses. There are surveillance video camera systems used which can simply classify the user behaviors to identify accident situations in a limited range of indoor areas where cameras are installed. To deal with this limitation, we propose a fusion algorithm to detect personalized unusual events via daily activity and vision patterns for a single household at home. We designed and implemented the proposed algorithm with the smartphone sensors, and a video camera installed in indoor areas. We evaluated individual activity and vision algorithms, and simulated the proposed fusion algorithm in scenarios.

     

     

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  • How to Cite

    Ahn, J., Park, H., Jung, J., & Lee, G. (2019). Unusual Event Detection Algorithm via Personalized Daily Activity and Vision Patterns for Single Households. International Journal of Engineering & Technology, 8(1.4), 533-544. https://doi.org/10.14419/ijet.v8i1.4.25465