Abstract: Most of traditional visual indoor navigation algorithms
and methods only consider the localization in ordinary daytime, while
we focus on the indoor re-localization in low light in the paper. As
RGB images are degraded in low light, less discriminative infrared
and depth image pairs are taken, as the input, by RGB-D cameras, the
most similar candidates, as the output, are searched from databases
which is built in the bag-of-word framework. Epipolar constraints can
be used to relocalize the query infrared and depth image sequence.
We evaluate our method in two datasets captured by Kinect2. The
results demonstrate very promising re-localization results for indoor
navigation system in low light environments.
Abstract: Human motion capture has become one of the major
area of interest in the field of computer vision. Some of the major
application areas that have been rapidly evolving include the
advanced human interfaces, virtual reality and security/surveillance
systems. This study provides a brief overview of the techniques and
applications used for the markerless human motion capture, which
deals with analyzing the human motion in the form of mathematical
formulations. The major contribution of this research is that it
classifies the computer vision based techniques of human motion
capture based on the taxonomy, and then breaks its down into four
systematically different categories of tracking, initialization, pose
estimation and recognition. The detailed descriptions and the
relationships descriptions are given for the techniques of tracking and
pose estimation. The subcategories of each process are further
described. Various hypotheses have been used by the researchers in
this domain are surveyed and the evolution of these techniques have
been explained. It has been concluded in the survey that most
researchers have focused on using the mathematical body models for
the markerless motion capture.