Abstract—Motion tracking is a vital component of study for a
video sequence having wide applications in object tracking,
coding and editing the videos and mosaicking . Getting the
actual motion for the videos existing in the real world though is
a difficult task but plays a pivotal role in designing a model for
any algorithm’s evaluation. We used an interactive computer
vision system  which provides annotation tool for labeling
and tracking the contours. Through the mutual work of user
interaction and the computer vision system, the input effort is
greatly reduced, simultaneously increasing the dependability of
the whole system as compared to the solely computer based
system. The ability of humans to easily segment and detect
difference between different frames has been utilized using the
human in loop methodology  by making use of a simple
camera. The paper experiments with the capabilities of the
system applied to indoor video sequence. This is the first paper
which evaluates the capabilities of image annotation supported
contour based object tracking with error analysis and
correction, explaining the significance of human in the error
incurred by the methodology. The paper studies the error
incurred by the system with movement from one frame to
another, supported by detailed simulations. The paper also
focuses on the reasons responsible for the error incurred by the
system mainly involving human intervention. Finally the paper
presents the correction of the error followed by the in depth
simulation indicating the in capabilities of the system on
deforming objects. This system can be effectively used to
analyze the error in motion tracking and further correcting the
error leading to flawless tracking.
Index Terms—Contour; Tracking; Error; Annotation; Optical flow component.
The authors are with National Institute of Technology, Warangal, India (firstname.lastname@example.org).
Cite:Amarjot Singh, Devinder Kumar, Akash Choubey, and Ketan Bacchuwar Srikrishna Karanam, "Annotation Supported Contour Based Object Tracking With Frame Based Error Analysis," International Journal of Machine Learning and Computing vol.2, no. 4, pp. 526-530, 2012.