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Detection, classification, and tracking of people and vehicles are fundamental processes in intelligent surveillance systems. The use of publicly available data set is the appropriate way to compare the relative merits of existing methods and to develop and assess new robust solutions. In this paper, we focus on the maritime domain and we describe the generation of boat classification data sets, containing images of boats automatically extracted by the ARGOS system, operating 24/7 in Venice, Italy. The data sets are unique in their nature, since they come from an incomparable environment like Venice, but they present very interesting challenges to vehicle classification, due to changes in the environmental conditions, boat wakes, waves, reflections, etc. We thus believe that robust techniques, validated through the ARGOS Boat Classification data sets, will improve the development and deployment of solutions in similar applications related to vehicle detection and classification.
Bloisi et al. (Sat,) studied this question.