Abstract:
Visual tracking is the process of estimating the location of an object in consecutive
video frames. It is a major problem in the domain of computer
vision with large set of applications such as tra c monitoring, robot planning,
surveillance, vehicle navigation and human computer interaction. The
problems related to object tracking are complex object shapes, noise in the
image, occlusion, and background clutter. The aim of this work is to develop
such technique which can track object in a complex environment. This work
presents a visual object tracking algorithm using an eigenspace representation.
Object appearance and spatial information is learned from a single
template using a non-linear subspace projection to arrive at a eigenspace
representation. This non-linear subspace representation provides a robust
and compact representation of the object. Localization is performed using
a similarity measure in non-linear eigenspace representation. A probabilistic
search strategy, based on particle lter, is employed to nd the region of an
object in each frame of the video sequence that best models the target object
in the subspace representation. Particle lter estimates the posterior distribution
using weighted samples. Increasing the number of samples increases
the estimation accuracy at the cost of increased computations. We, therefore
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propose a novel kernel subspace integral image framework, which allows the
tracker to densely sample the state space without loosing computational ef-
ciency. The proposed tracker is tested on number of challenging sequences
to demonstrate the performance.
