(Georgia Institute of Technology, 2015-01)
Bettadapura, Vinay; Thomaz, Edison; Parnam, Aman; Abowd, Gregory D.; Essa, Irfan
The pervasiveness of mobile cameras has resulted in a
dramatic increase in food photos, which are pictures re-
flecting what people eat. In this paper, we study how tak-
ing pictures of what we eat in restaurants can be used for
the purpose of automating food journaling. We propose to
leverage the context of where the picture was taken, with ad-
ditional information about the restaurant, available online,
coupled with state-of-the-art computer vision techniques to
recognize the food being consumed. To this end, we demon-
strate image-based recognition of foods eaten in restaurants
by training a classifier with images from restaurant’s on-
line menu databases. We evaluate the performance of our
system in unconstrained, real-world settings with food im-
ages taken in 10 restaurants across 5 different types of food
(American, Indian, Italian, Mexican and Thai).
Activity recognition in the home has been long recognized as
the foundation for many desirable applications in fields such
as home automation, sustainability, and healthcare. However,
building a practical home activity monitoring system
remains a challenge. Striking a balance between cost, privacy,
ease of installation and scalability continues to be an
elusive goal. In this paper, we explore infrastructure-mediated
sensing combined with a vector space model learning approach
as the basis of an activity recognition system for the
home. We examine the performance of our single-sensor
water-based system in recognizing eleven high-level activities
in the kitchen and bathroom, such as cooking and shaving.
Results from two studies show that our system can estimate
activities with overall accuracy of 82.69% for one individual
and 70.11% for a group of 23 participants. As far
as we know, our work is the first to employ infrastructure-mediated
sensing for inferring high-level human activities in
a home setting.