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Recent years have seen an increased use of Unmanned Aerial Vehicles (UAV) with video-recording capability for Maritime Domain Awareness (MDA) and other surveillance operations. In order for these efforts to be effective, there is a need to develop automated algorithms to process the full-motion videos (FMV) captured by UAVs in an efficient and timely manner to extract meaningful information that can assist human analysts and decision makers. This paper presents a generalizeable marine object detection system that is specifically designed to process raw video footage streaming from UAVs in real-time. Our approach does not make any assumptions about the object and/or background characteristics because, in the MDA domain, we encounter varying background and foreground characteristics such as boats, bouys and ships of varying sizes and shapes, wakes, white caps on water, glint from the sun, to name but a few. Our efforts rely on basic signal processing and machine learning approaches to develop a generic object detection system that maintains a high level of performance without making prior assumptions about foreground-background characteristics and does not experience abrupt performance degradation when subjected to variations in lighting, background characteristics, video quality, abrupt changes in video perspective, size, appearance and number of the targets. In the following report, in addition to our marine object detection system, we present representative object detection results on some real-world UAV full-motion video data.

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The ability to detect the horizon on a real-time basis in full-motion video is an important capability to aid and facilitate real-time processing of full-motion videos for the purposes such as object detection, recognition and other video/image segmentation applications. In this paper, we propose a method for real-time horizon detection that is designed to be used as a front-end processing unit for a real-time marine object detection system that carries out object detection and tracking on full-motion videos captured by ship/harbor-mounted cameras, Unmanned Aerial Vehicles (UAVs) or any other method of surveillance for Maritime Domain Awareness (MDA). Unlike existing horizon detection work, we cannot assume a priori the angle or nature (for e.g. straight line) of the horizon, due to the nature of the application domain and the data. Therefore, the proposed real-time algorithm is designed to identify the horizon at any angle and irrespective of objects appearing close to and/or occluding the horizon line (for e.g. trees, vehicles at a distance) by accounting for its non-linear nature. We use a simple two-stage hierarchical methodology, leveraging color-based features, to quickly isolate the region of the image containing the horizon and then perform a more fine-grained horizon detection operation. In this paper, we present our real-time horizon detection results using our algorithm on real-world full-motion video data from a variety of surveillance sensors like UAVs and ship mounted cameras confirming the real-time applicability of this method and its ability to detect horizon with no a priori assumptions.