This Thesis reports on a study of the ways in which officer cadets study the Collision Regulations at Britannia Royal Naval College (BRNC), which is widely held to be difficult and, anecdotally, leads to a high failure rate on testing. The research is opportunistic since my background both in the Royal Navy and as a civilian lecturer at BRNC facilitated unprecedented access and the potential for original discoveries. I took a mixed-methods approach with both quantitative and qualitative features within a pragmatic paradigm, based upon an eclectic selection of methodological models to suit the case, in order to achieve a measure of triangulation upon the learning with the aim of understanding it: I chose not to be bound by anyone theoretical perspective. In order to facilitate this I set five research questions. My aim was to add to the existing body of research on learning in order to generate some practical recommendations for improving learning the Collision Regulations to the benefit of the College and the wider seafaring community. I reached four principal conclusions: first, that there were a number of practical ways in which the learning could be improved; second, that the biggest single factors in determining success or failure were the attitude towards learning and the confidence exhibited by the students; third, that the failure-rate is not as high as expected and the majority of students at this level do not find it as difficult as anecdotal and historical evidence suggests and fourth, that to describe and understand the unique context of learning at BRNC requires a combination of several theoretical approaches to learning.

With the advent of modem high-speed passenger ferries and the general increase in maritime traffic, both commercial and recreational, marine safety is becoming an increasingly important issue. From lightweight catamarans and fishing trawlers to container ships and cruise liners one question remains the same. Is anything in the way? This question is addressed in this thesis. Through the use of image processing techniques applied to video sequences of maritime scenes the images are segmented into two regions, sea and object. This is achieved using statistical measures taken from the histogram data of the images. Each segmented object has a feature vector built containing information including its size and previous centroid positions. The feature vectors are used to track the identified objects across many frames. With information recorded about an object's previous motion its future motion is predicted using a least squares method. Finally a high-level rule-based algorithm is applied in order to estimate the collision risk posed by each object present in the image. The result is an image with the objects identified by the placing of a white box around them. The predicted motion is shown and the estimated collision risk posed by that object is displayed. The algorithms developed in this work have been evaluated using two previously unseen maritime image sequences. These show that the algorithms developed here can be used to estimate the collision risk posed by maritime objects.