Real-time object detection can greatly help in automatic ship recognition. Most often though, image-based ship classifiers do not take into account all the class-specific geometric information available in the perspective projection performed by coastal surveillance cameras. This paper introduces therefore a novel Bayesian filter to jointly track the vessel kinematic states and classify it by fusing the labeled bounding boxes detected on incoming frames originating from multiple fully calibrated cameras. Simulation results show that the proposed recursive filter is able to improve the overall classification accuracy compared to a single-frame ship classifier. Finally, we were able to track and consistently classify a real-world ship using coastal surveillance cameras surrounding the Guanabara Bay at Rio de Janeiro.

Multi-sensor-based surveillance systems with overlapping detection areas must address the challenge of associating objects detected by different sensors and accurately determining whether they correspond to the same real-world entity. Notably, different sensors may have distinct state vectors (e.g. AIS, radar, and passive sonar), and in some cases, information from systems such as AIS may be unavailable. Additionally, the area under surveillance may be vast and contain a large number of vessels, leading to a high computational cost for track association. This study aims to simulate the aforementioned scenario, which is commonly observed along the Brazilian coast today, and to apply a combination of association and fusion techniques to enhance problem-solving. Some models are classical, while others introduce contextual adaptations or modifications to their original conditions of use. Statistical results indicate that contextualization significantly reduces the execution time of multi-sensor association algorithms, while adaptations in classical algorithms improve target fusion across different sea conditions.