Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey

TL;DR

This survey reviews video processing techniques for maritime object detection and tracking using electro-optical sensors, highlighting challenges, approaches, and recent advancements for autonomous ship navigation.

Contribution

It provides a comprehensive taxonomy and comparison of various EO video processing methods, including new evaluations on the Singapore Maritime Dataset.

Findings

Background motion due to waves affects detection accuracy

Dynamic background subtraction is crucial for robust tracking

Computer vision methods show promise for maritime EO data

Abstract

We present a survey on maritime object detection and tracking approaches, which are essential for the development of a navigational system for autonomous ships. The electro-optical (EO) sensor considered here is a video camera that operates in the visible or the infrared spectra, which conventionally complement radar and sonar and have demonstrated effectiveness for situational awareness at sea has demonstrated its effectiveness over the last few years. This paper provides a comprehensive overview of various approaches of video processing for object detection and tracking in the maritime environment. We follow an approach-based taxonomy wherein the advantages and limitations of each approach are compared. The object detection system consists of the following modules: horizon detection, static background subtraction and foreground segmentation. Each of these has been studied extensively in maritime situations and has been shown to be challenging due to the presence of background motion especially due to waves and wakes. The main processes involved in object tracking include video frame registration, dynamic background subtraction, and the object tracking algorithm itself. The challenges for robust tracking arise due to camera motion, dynamic background and low contrast of tracked object, possibly due to environmental degradation. The survey also discusses multisensor approaches and commercial maritime systems that use EO sensors. The survey also highlights methods from computer vision research which hold promise to perform well in maritime EO data processing. Performance of several maritime and computer vision techniques is evaluated on newly proposed Singapore Maritime Dataset.