M.Ing. / There is a growing need to enhance situation awareness in the maritime environment utilizing new and current technologies. There are numerous ways to enhance situation awareness by employing long-range vision detection systems, data fusion techniques, such as combining radar and automatic identification system (AIS) data and data mining techniques that allow for filtering out anomalies. With the proliferation of high-quality video equipment and cheaper and faster computational machines, there is an increasing need for automated video surveillance as the amount of information available to the operator for processing is overwhelming. It is therefore necessary that only crucial information that may negatively impact mission effectiveness be presented to the operator. Whilst performing surveillance one would be interested in monitoring other surface vessels within the sensor coverage. The detection and tracking of small and slow moving targets having low signal-to-noise ratios is of interest in the maritime environment. This is particularly challenging as influences from the natural environment, such as sea states, glint, whitecaps and clutter, on a target is captured during image acquisition and this has adverse effects on the tracking of a target. A grey-scale based target tracking algorithm using the particle filter framework was developed and tested in MATLAB® (R2008a). The main focus of the work is on the use of dynamic models in a particle filtering framework. The dynamic model contributes to the propagation of the particles in a particle filtering framework of the target grey-scale distribution. The dynamic models investigated are the constant velocity model and an acceleration model. The algorithm was tested with real-world image sequences in the maritime environment. The targets were tracked for the duration of the image sequence and the dynamic model that accounted for acceleration yielded better results when analysing the position error between the estimated position and the ground truth data points. A slight improvement in this error makes a significant difference on tracking a target as targets in the maritime environment context are small. The future scope of the work would then include accounting for more features of the target such as edge cues and/or implementing adaptive observation models to improve the accuracy, stability and robustness of the algorithm for real-time applications.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:10429 |
| Date | 24 October 2012 |
| Creators | Joseph, Suja Maria |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
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