'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
The term smart environment refers to physical
spaces equipped with sensors feeding into adaptive algorithms
that enable the environment to become sensitive and
responsive to the presence and needs of its occupants. People
with special needs, such as the elderly or disabled people,
stand to benefit most from such environments as they offer
sophisticated assistive functionalities supporting independent
living and improved safety. In a smart environment, the key
issue is to sense the location and identity of its users. In this
paper, we intend to tackle the problems of detecting and
tracking humans in a realistic home environment by exploiting
the complementary nature of (synchronized) color and depth
images produced by a low-cost consumer-level RGB-D
camera. Our system selectively feeds the complementary data
emanating from the two vision sensors to different algorithmic
modules which together implement three sequential
components: (1) object labeling based on depth data
clustering, (2) human re-entry identification based on
comparing visual signatures extracted from the color (RGB)
information, and (3) human tracking based on the fusion of
both depth and RGB data. Experimental results show that this
division of labor improves the system’s efficiency and
classification performance