Beyond Land: A Review of Benchmarking Datasets, Algorithms, and Metrics for Visual-Based Ship Tracking

Abstract

Object tracking has gained much interest in the last few years, especially in the context of multiple object tracking. Many datasets used for tracking provide video sequences of people and objects in very different contexts. Although it has been attracting much attention, no dataset or tracking algorithm has been applied to coastal surveillance and ship tracking. Besides image/video-based tracking technologies, other technologies, such as radar and automatic identification systems (AISs), are also used for this task, especially in maritime applications. In the AIS case, commonly known issues, such as information omission, remain to be dealt with. As for radars, the most important issue is the impossibility of identifying the ship type/class and correlating it with AIS information. However, image/video-based solutions can be combined with these technologies to mitigate or even solve these issues. This work aims to review the most recent datasets and state-of-the-art tracking algorithms (also known as trackers) for single or multiple objects tracking for objects in general and its possibilities for maritime scenarios. The goal is to gain insights for developing novel datasets; benchmarking metrics; and mainly, novel ship tracking algorithms.