Peer-Reviewed Conference Papers
T.Albrecht, T.Tan, G.West, T.Ly, (2010).
Omnidirectional Video Stabilisation On a Virtual Camera Using Sensor Fusion.
Proceedings of the 11th International Conference on Control, Autoation, Robotics and Vision.
Accepted for publication.
Abstract: This paper presents a method for robustly stabilising omnidirectional video
given the presence of significant rotations and translations by creating a virtual camera
and using a combination of sensor fusion and scene tracking. Real time rotational movements
of the camera are measured by an Inertial Measurement Unit (IMU), which provides an initial
estimate of the ego-motion of the camera platform. Image registration is then used to refine
these estimates. The calculated ego-motion is then used to adjust an extract of the
omnidirectional video, forming a virtual camera that is focused on the scene. Experiments
show the technique is effective under challenging ego-motions and overcomes deficiencies
that are associated with unimodal approaches making it robust and suitable to be used in
many surveillance applications.
T.Albrecht, G.A.W. West, T.Tan, T.Ly, (2010).
Multiple Views Tracking of Maritime Targets.
Proceedings of the International Conference on Digital Image Computing: Techniques
and Applications (DICTA).
Abstract: This paper explores techniques for multiple views target tracking in a
maritime environment using a mobile surveillance platform. We utilise an omnidirectional
camera to capture full spherical video and use an Inertial Measurement Unit (IMU) to
estimate the platform’s ego-motion. For each target a part of the omnidirectional video
is extracted, forming a corresponding set of virtual cameras. Each target is then tracked
using a dynamic template matching method and particle filtering. Its predictions are then
used to continuously adjust the orientations of the virtual cameras, keeping a lock on
the targets. We demonstrate the performance of the application in several real-world
maritime settings.
T.Albrecht, T.Tan, G.West, T.Ly, S.Moncrieff(2011).
Vision-based Attention in Maritime Environments.
Proceedings of the Eighth International Conference on Information, Communications
and Signal Processing
Abstract: This paper presents a saliency inspired visual attention technique
for maritime scenes. The main focus is on finding regions in images which there
is a high likelihood of a maritime object being present. Experimentation has
shown that many regional and global features are required because no single
feature can reliably detect these objects. Examples of the features used are
right angle corner detectors, edge density, and colour difference. A Gaussian
classifier has been used to produce an Attention Map of pixel responses.
Experiments using ground truthed images show the technique is effective on a
large set of images of maritime scenes and is better at detecting maritime
objects than existing generic salient detectors.
T.Albrecht, G.West, T.Tan, T.Ly, (2011).
Visual Maritime Attention Using Multiple Low-Level Features
and Naïve Bayes Classification.
Best Student Paper at the International Conference on Digital Image
Computing: Techniques and Applications (DICTA).
Abstract: This paper presents a framework for Visual Attention Detection
in maritime scenes. The focus is to provide an early processing stage for
high resolution images captured by maritime surveillance platforms. The framework
groups multiple low-level features that are designed specifically for maritime
scenarios with different distance measurements. Integrated in the framework is
a detector for sea and sky that aids in background segmentation. A Naïve Bayes
Classifier is used to produce Attention Maps of the input images. Experiments using
ground truthed images show the technique is effective on a large dataset of
maritime images and outperforms state of the art generic saliency detectors.