Detection of unusual fish trajectories from underwater videos

Fish behaviour analysis is a fundamental research area in marine ecology as it is helpful for detecting environmental changes by observing unusual fish patterns or new fish behaviours. The traditional way of analysing fish behaviour is by visual inspection using human observers, which is very time consuming and also limits the amount of data that can be processed. Therefore, there is a need for automatic algorithms to identify fish behaviours by using computer vision and machine learning techniques. The aim of this thesis is to help marine biologists with their work. We focus on behaviour understanding and analysis of detected and tracked fish with unusual behaviour detection approaches. Normal fish trajectories exhibit frequently observed behaviours while unusual trajectories are outliers or rare trajectories. This thesis proposes 3 approaches to detecting unusual trajectories: i) a filtering mechanism for normal fish trajectories, ii) an unusual fish trajectory classification method using clustered and labelled data and iii) an unusual fish trajectory classification approach using a clustering based hierarchical decomposition. The rule based trajectory filtering mechanism is proposed to remove normal fish trajectories which potentially helps to increase the accuracy of the unusual fish behaviour detection system. The aim is to reject normal fish trajectories as much as possible while not rejecting unusual fish trajectories. The results show that this method successfully filters out normal trajectories with a low false negative rate. This method is useful to assist building a ground truth data set from a very large fish trajectory repository, especially when the amount of normal fish trajectories greatly dominates the unusual fish trajectories. Moreover, it successfully distinguishes true fish trajectories from false fish trajectories which result from errors by the fish detection and tracking algorithms. A key contribution of this thesis is the proposed flat classifier, which uses an outlier detection method based on cluster cardinalities and a distance function to detect unusual fish trajectories. Clustered and labelled data are used to select feature sets which perform best on a training set. To describe fish trajectories 10 groups of trajectory descriptions are proposed which were not previously used for fish behaviour analysis. The proposed flat classifier improved the performance of unusual fish detection compared to the filtering approach. The performance of the flat classifier is further improved by integrating it into a hierarchical decomposition. This hierarchical decomposition method selects more specific features for different trajectory clusters which is useful considering the trajectory variety. Significantly improved results were obtained using this hierarchical decomposition in comparison to the flat classifier. This hierarchical framework is also applied to classification of more general imbalanced data sets which is a key current topic in machine learning. The experiments showed that the proposed hierarchical decomposition method is significantly better than the state of art classification methods, other outlier detection methods and unusual trajectory detection methods. Furthermore, it is successful at classifying imbalanced data sets even though the majority and minority classes contain varieties, and classes overlap which is frequently seen in real-world applications. Finally, we explored the benefits of active learning in the context of the hierarchical decomposition method, where active learning query strategies choose the most informative training data. A substantial performance gain is possible by using less labelled training data compared to learning from larger labelled data sets. Additionally, active learning with feature selection is investigated. The results show that feature selection has a positive effect on the performance of active learning. However, we show that random selection can be as effective as popular active learning query strategies in combination with active learning and feature selection, especially for imbalanced set classification