Object Detection and Tracking for ASV

In this thesis automatic Object Detection system is presented. Object Detection is performed by different algorithms. As reading many literature we have observed that detecting objects in particular video sequence or by any surveillance cameras is a really challenging task in computer vision application because in sea the atmosphere affects a lot in the detection. Therefore we felt that there can be a wide range of possibilities are open in relation to detection. In order to improve the object detection, we developed image stabilization software on top of the image acquisition. First image stabilization has been performed over the raw data of ROAZ II. After achieving stabled video or images, object detection algorithm is performed using color based segmentation. Field tests have been performed with a data set from the ROAZ-II and during it shows the effectiveness of the approach. And system is able to achieve object detection in video or images with high accuracy

Preventing Armageddon I: Enhancing America's Border & Port Security After 9/11; Strategic Insights, v. 3 issue 11 (November 2004)

This article appeared in Strategic Insights, v.3 issue 11 (November 2004)Approved for public release; distribution is unlimited

Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review

Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The arti

A horizon line annotation tool for streamlining autonomous sea navigation experiments

Horizon line (or sea line) detection (HLD) is a critical component in multiple marine autonomous navigation tasks, such as identifying the navigation area (i.e., the sea), obstacle detection and geo-localization, and digital video stabilization. A recent survey highlighted several weaknesses of such detectors, particularly on sea conditions lacking from the most extensive dataset currently used by HLD researchers. Experimental validation of more robust HLDs involves collecting an extensive set of these lacking sea conditions and annotating each collected image with the correct position and orientation of the horizon line. The annotation task is daunting without a proper tool. Therefore, we present the first public annotation software with tailored features to make the sea line annotation process fast and easy. The software is available at: https://drive.google.com/drive/folders/1c0ZmvYDckuQCPIWfh_70P7E1A_DWlIvF?usp=sharin

JRC - Alenia Aeronautica Coupled UAS and Spaceborne SAR Campaign in Italy

The European maritime area is one of Europe’s most important assets with regard to resources, security and ultimately prosperity of the Member States. A significant part of Europe’s economy relies directly or indirectly on it. It is not just the shipping or fisheries industries and their related activities. It is also shipbuilding and ports, marine equipment and offshore energy, maritime and coastal tourism, aquaculture, submarine telecommunications, blue biotech and the protection of the marine environment. The European maritime area faces several risks and threats posed by unlawful activities, such as drugs trafficking, smuggling, illegal immigration, organised crime and terrorism. Piracy in international waters also constitutes a threat to Europe since it can disrupt the maritime transport chain. These risks and threats can endanger human lives, marine resources and the environment, as well as significantly disrupt the transport chain and global and local security. It is anticipated that these risks and threats will endure in the mid and long run. In order to keep Europe as a world leader in the global maritime economy, an effective integrated/interoperable, sustainable maritime surveillance system and situational awareness are needed. A significant number of unlawful maritime activities, such as illegal immigration, drugs trafficking, smuggling, piracy and terrorism involve mainly small boats, because small boats are faster and more difficult to detect using conventional means. Hence, it is very important to find out the feasibility of using Unmanned Aerial Systems (UAS) for small boat detection, tracking, classification and identification, as well as to study the potential of UAS for maritime surveillance. Since 2010 the EC-JRC has carried out a number of UAS maritime surveillance campaigns to study the potential of UAS for maritime surveillance, in particular for small boat detection. This report presents the results and conclusions of the JRC - Alenia Aeronautica Coupled UAS and Spaceborne SAR campaign carried out in Oct. 2010 in Porto Corallo, Sardinia, Italy.JRC.G.4-Maritime affair

Unsupervised maritime target detection

The unsupervised detection of maritime targets in grey scale video is a difficult problem in maritime video surveillance. Most approaches assume that the camera is static and employ pixel-wise background modelling techniques for foreground detection; other methods rely on colour or thermal information to detect targets. These methods fail in real-world situations when the static camera assumption is violated, and colour or thermal data is unavailable. In defence and security applications, prior information and training samples of targets may be unavailable for training a classifier; the learning of a one class classifier for the background may be impossible as well. Thus, an unsupervised online approach that attempts to learn from the scene data is highly desirable. In this thesis, the characteristics of the maritime scene and the ocean texture are exploited for foreground detection. Two fast and effective methods are investigated for target detection. Firstly, online regionbased background texture models are explored for describing the appearance of the ocean. This approach avoids the need for frame registration because the model is built spatially rather than temporally. The texture appearance of the ocean is described using Local Binary Pattern (LBP) descriptors. Two models are proposed: one model is a Gaussian Mixture (GMM) and the other, referred to as a Sparse Texture Model (STM), is a set of histogram texture distributions. The foreground detections are optimized using a Graph Cut (GC) that enforces spatial coherence. Secondly, feature tracking is investigated as a means of detecting stable features in an image frame that typically correspond to maritime targets; unstable features are background regions. This approach is a Track-Before-Detect (TBD) concept and it is implemented using a hierarchical scheme for motion estimation, and matching of Scale- Invariant Feature Transform (SIFT) appearance features. The experimental results show that these approaches are feasible for foreground detection in maritime video when the camera is either static or moving. Receiver Operating Characteristic (ROC) curves were generated for five test sequences and the Area Under the ROC Curve (AUC) was analyzed for the performance of the proposed methods. The texture models, without GC optimization, achieved an AUC of 0.85 or greater on four out of the five test videos. At 50% True Positive Rate (TPR), these four test scenarios had a False Positive Rate (FPR) of less than 2%. With the GC optimization, an AUC of greater than 0.8 was achieved for all the test cases and the FPR was reduced in all cases when compared to the results without the GC. In comparison to the state of the art in background modelling for maritime scenes, our texture model methods achieved the best performance or comparable performance. The two texture models executed at a reasonable processing frame rate. The experimental results for TBD show that one may detect target features using a simple track score based on the track length. At 50% TPR a FPR of less than 4% is achieved for four out of the five test scenarios. These results are very promising for maritime target detection

Compendium of Applications Technology Satellite user experiments

The achievements of the user experiments performed with ATS satellites from 1967 to 1973 are summarized. Included are fixed and mobile point to point communications experiments involving voice, teletype and facsimile transmissions. Particular emphasis is given to the Alaska and Hawaii satellite communications experiments. The use of the ATS satellites for ranging and position fixing of ships and aircraft is also covered. The structure and operating characteristics of the various ATS satellite are briefly described

SeaVipers - Computer Vision and Inertial Position Reference Sensor System (CVIPRSS)

This work describes the design and development of an optical, Computer Vision (CV) based sensor for use as a Position Reference System (PRS) in Dynamic Positioning (DP). Using a combination of robotics and CV techniques, the sensor provides range and heading information to a selected reference object. The proposed optical system is superior to existing ones because it does not depend upon special reflectors nor does it require a lengthy set-up time. This system, the Computer Vision and Inertial Position Reference Sensor System (CVIPRSS, pronounced \nickname), combines a laser rangefinder, infrared camera, and a pan--tilt unit with the robust TLD (Tracking--Learning--Detection) object tracker. In this work, a \nickname ~prototype is evaluated, showing promising results as viable PRS with research, commercial, and industrial applications