A new annotated dataset for boat detection and re-identification

Sea surveillance and monitoring is receiving an increasing attention in the last years especially in specific areas and countries, such as the Mediterranean sea. The automatic detection of boats is a strategic activity that can be realized by means of satellite or RGB images. Satellite monitoring requires expensive hardware (Lidar, Landscape, and so on) and high-level efforts and permissions; on the other hand, satellite images do not allow a high level of resolution able to provide information about the number of passengers on the boat, or its name/ID. The use of RGB cameras placed in specific coastal sites allows to realize a low-cost and scalable monitoring system. Convolutional Neural Networks (CNN) are commonly used in application of object detection, classification and tracking. In addition, they are used also for re-identification, mainly applied to people and cars, rarely to boats. The use of neural networks requires a specific training based on labeled datasets. At present there are just a few annotated datasets for boat detection, while nothing is present for boat re-identification. Here we propose a new dataset for boat re-identification; it is composed by annotated images of boats acquired during last summer in the Marine Protected Area (MPA) of Porto Cesareo. In addition, we propose as baseline the results obtained by the application of a Discriminatively Learned CNN for boat re-identification.