TITLE:
Rebound of Region of Interest (RROI), a New Kernel-Based Algorithm for Video Object Tracking Applications
AUTHORS:
Andres Alarcon Ramirez, Mohamed Chouikha
KEYWORDS:
Video Object Tracking, Cluttered Conditions, Kernel-Based Algorithm
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.5 No.4,
October
7,
2014
ABSTRACT: This paper presents a new
kernel-based algorithm for video object tracking called rebound of region of
interest (RROI). The novel algorithm uses a rectangle-shaped section as region
of interest (ROI) to represent and track specific objects in videos. The
proposed algorithm is constituted by two stages. The first stage seeks to
determine the direction of the object’s motion by analyzing the changing
regions around the object being tracked between two consecutive frames. Once
the direction of the object’s motion has been predicted, it is initialized an
iterative process that seeks to minimize a function of dissimilarity in order
to find the location of the object being tracked in the next frame. The main
advantage of the proposed algorithm is that, unlike existing kernel-based
methods, it is immune to highly cluttered conditions. The results obtained by
the proposed algorithm show that the tracking process was successfully carried
out for a set of color videos with different challenging conditions such as
occlusion, illumination changes, cluttered conditions, and object scale
changes.