Tracking Ships from Fast Moving Camera through Image Registration

Fefilatyev, Sergiy; Goldgof, Dmitry B.; Lembke, Chad

doi:10.1109/icpr.2010.854

Cited by 31 publications

(17 citation statements)

References 13 publications

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“…Fefilatyev et. al [16] used Gaussian low pass filtering in a narrow strip below horizon, followed by a color gradient filter to obtain regions of high color variations and finally applied a threshold computed using Otsu's method [72] to obtain the background.…”

Section: B Static Background Subtractionmentioning

confidence: 99%

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Prasad

Rajan

Rachmawati³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

330

168

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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 approachbased 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 Marine Dataset.• the difficulty in modeling the dynamics of water (including waves, wakes and foams) for background subtraction and detection of foreground objects, • variations in object appearances due to distance and angle of viewing, and • changes in illumination and weather conditions, such as due to clouds, sunshine, rain, glint, etc.

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Section: B Static Background Subtractionmentioning

confidence: 99%

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey

Prasad

Rajan

Rachmawati³

et al. 2017

IEEE Trans. Intell. Transport. Syst.

330

168

View full text Add to dashboard Cite

show abstract

“…The main drawback is the use of multiple hard coded parameters, tuned according to the experience of the authors. Histogram of Oriented Gradients (HOG) are used in [12]. Since the calculation of the detection features involves a significant amount of computational resources, realtime performance can be obtained only adopting hardware acceleration with programmable components (e.g., FPGAs).…”

Section: Related Workmentioning

confidence: 99%

Enhancing Automatic Maritime Surveillance Systems With Visual Information

Bloisi

Previtali

Pennisi

et al. 2017

IEEE Trans. Intell. Transport. Syst.

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Automatic surveillance systems for the maritime domain are becoming more and more important due to a constant increase of naval traffic and to the simultaneous reduction of crews on decks. However, available technology still provides only a limited support to this kind of applications. In this paper, a modular system for intelligent maritime surveillance, capable of fusing information from heterogeneous sources, is described. The system is designed to enhance the functions of the existing Vessel Traffic Services systems and to be deployable in populated areas, where radar-based systems cannot be used due to the high electromagnetic radiation emissions. A quantitative evaluation of the proposed approach has been carried out on a large and publicly available data set of images and videos, collected from multiple real sites, with different light, weather, and traffic conditions.

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“…Video-based techniques where sequences of image frames are analysed are well studied in road vehicle detection, but for vessel detection, video analysis still remains a challenging and complex task. Recently, several papers discussed the promising usage of video cameras to perform detection of vessels (Arshad et al, 2010, Bao et al, 2013, Fefilatyev et al, 2010, Hu et al, 2011, Rodriguez Sullivan and Shah, 2008). Camera-based vessel detection is attractive due to its low cost and ease of management, both in installation and maintenance.…”

Section: Introductionmentioning

confidence: 99%