Target identification in a complex maritime scene

Voles, P.; Teal, M.K.; Sanderson, James G.

doi:10.1049/ic:19990585

Cited by 10 publications

(9 citation statements)

References 0 publications

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“…The detected change image was then analyzed to find if the changes correlate to the prediction of Bayesian background model [78] or are they likely to be foreground. The regions classified Relatively stationary pixels [17], [18], [37], [88]− [90] Temporal filter extracts statistical representative of background Sliding temporal window, change from reference, spatial smoothing Simple, computation efficient, online learning Cannot deal with highly dynamic backgrounds, e.g., wakes Spatio-temporal filtering approaches [22], [39], [40] Background is modelled as low spatial frequency component, albeit with temporal variation Sliding temporal window and fixed spatial window (blobs or neighborhood) are used for filter parameters' update Simple, online learning, computation efficient, robust to small dynamics and illumination variation Cannot deal with highly dynamic backgrounds, e.g., wakes GMM [12], [77] Intensities at a pixel as mixture of Gaussian distributions Multi-step approaches [13], [21] Combination of more than one technique More robust and versatile, often made adaptive and capable of dealing with wakes Complicated, computation intensive, slow due to frequent feedback -feed-forward steps as foreground were then used with color-based segmentation approach to further strengthen the foreground estimation and thus contribute to more robust background detection. The background thus determined was used to update the reference stationary image and the Bayesian background model.…”

Section: Dynamic 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.

284

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: Dynamic 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.

284

168

View full text Add to dashboard Cite

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“…In this direction, Voles et al [29] proposed a method for object detection based on anisotropic diffusion, which however presents high computational cost and performs well only for horizontal and vertical edges. In [30] is presented a method that fuses a foreground object detection technique with image color segmentation to improve accuracy.…”

Section: Maritime Surveillancementioning

confidence: 99%

Multi-sensor target detection and tracking system for sea ground borders surveillance

Agrafiotis

Doulamis

Georgopoulos

2014

Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

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Border safety is a critical part of national and European security. This paper presents a vision-based system for ground and maritime surveillance using fixed and moving PTZ cameras. This system is intended to be used as an early warning system by local authorities. For the ground surveillance scenario, we introduce a stable human tracker able to efficiently cope with the trade-off between model stability and adaptability. More specifically, we adopt probabilistic mixture models like the Gaussian Mixture Models (GMMs) which exploit geometric properties for background modelling. Then, we integrate iterative motion information methods, concerned by shape and time properties, to estimate image regions of high confidence for updating the background model. For the maritime surveillance scenario for ship detecting and tracking, the system incorporates a visual attention method exploiting low-level image features with an online adaptable neural network tracker. No assumptions about environmental or visual conditions are made. System performance was evaluated in real time for robustness compared to dynamically changing visual conditions with videos from cameras placed at a test area near Athens for the ground scenario and at Venetian port of Chania.

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“…This method often results in imprecise segmentation results. Voles et al [21] improve the method presented in [20] by adding motion information obtained using frame differencing; the algorithm designed is purely pixel-based and therefore fails to segment larger maritime objects.…”

Section: Traditional Approachmentioning

confidence: 99%

Detection and tracking of moving objects in a maritime environment using level set with shape priors

Frost

Tapamo

2013

J Image Video Proc

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Over the years, maritime surveillance has become increasingly important due to the recurrence of piracy. While surveillance has traditionally been a manual task using crew members in lookout positions on parts of the ship, much work is being done to automate this task using digital cameras coupled with a computer that uses image processing techniques that intelligently track object in the maritime environment. One such technique is level set segmentation which evolves a contour to objects of interest in a given image. This method works well but gives incorrect segmentation results when a target object is corrupted in the image. This paper explores the possibility of factoring in prior knowledge of a ship's shape into level set segmentation to improve results, a concept that is unaddressed in maritime surveillance problem. It is shown that the developed video tracking system outperforms level set-based systems that do not use prior shape knowledge, working well even where these systems fail.

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Target identification in a complex maritime scene

Cited by 10 publications

References 0 publications

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

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

Multi-sensor target detection and tracking system for sea ground borders surveillance

Detection and tracking of moving objects in a maritime environment using level set with shape priors

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