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The goal of electronic monitoring of longline fishing is to visually monitorthe fish catching activities on fishing vessels based on cameras, either forregulatory compliance or catch counting. The previous hierarchicalclassification method demonstrates efficient fish species identification ofcatches from longline fishing, where fishes are under severe deformation andself-occlusion during the catching process. Although the hierarchicalclassification mitigates the laborious efforts of human reviews by providingconfidence scores in different hierarchical levels, its performance dropsdramatically under the class incremental learning (CIL) scenario. A CIL systemshould be able to learn about more and more classes over time from a stream ofdata, i.e., only the training data for a small number of classes have to bepresent at the beginning and new classes can be added progressively. In thiswork, we introduce a Hierarchical Class Incremental Learning (HCIL) model,which significantly improves the state-of-the-art hierarchical classificationmethods under the CIL scenario.

HCIL: Hierarchical Class Incremental Learning for Longline Fishing Visual Monitoring