  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.
