Motion Projection for Floating Object Detection

Wei, Zhaoyi; Lee, Dah-Jye; Jilk, David J.; Schoenberger, Robert B.

doi:10.1007/978-3-540-76856-2_15

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…The algorithm is briefly reviewed in this paper. One can refer to [7] for further details of the algorithm. At each scale, the algorithm includes several stages: candidate pixel selection, motion estimation/analysis, and spatio-temporal smoothing.…”

Section: Algorithm Reviewmentioning

confidence: 99%

“…Existing classical motion estimation algorithms such as block matching [8,9] and optical flow [10,11] have difficulty in this case because of the variant size of the object and abrupt brightness changes. A motion correspondence algorithm called the "motion projection algorithm" is proposed in [7] to estimate motion with high accuracy. Essentially, this algorithm aims at finding the best correspondence of one pixel across two neighboring frames.…”

Section: Algorithm Reviewmentioning

confidence: 99%

“…In this paper, a floating mine detection algorithm and a GPUs based computation framework was presented. Our previous works in [7] presented an algorithm which reliably extracts suspicious buoy-like floating objects from a video sequence. This algorithm extracts the candidates by examining the appearance and motion information.…”

Section: Introductionmentioning

confidence: 99%

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GPU Based Real-time Floating Object Detection System

Yang¹,

Meng²

2016

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

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A GPU-based floating object detection scheme is presented in this paper which is designed for floating mine Detection tasks. This system uses contrast and motion information to eliminate as many false positives as possible while avoiding false negatives. The GPU computation platform is deployed to allow detecting objects in real-time. From the experimental results, it is shown that with certain configuration, the GPU-based scheme can speed up the computation up to one thousand times compared to the CPU-based scheme.

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Section: Algorithm Reviewmentioning

confidence: 99%

Section: Algorithm Reviewmentioning

confidence: 99%

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GPU Based Real-time Floating Object Detection System

Yang¹,

Meng²

2016

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

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“…Thus, algorithms for analyzing video contents which require little or no human input are a good solution. Video surveillance systems focus on background modeling, moving object classification and tracking [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

An adaptive moving ship detection and tracking based on edge information and morphological operations

2011

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Moving ship detection and tracking is the key technology in the intelligence coastal surveillance system. In this paper we present an adaptive and improved method to accurately detect and monitor ships within the area of interest. It is an advanced version of the previous works done regarding moving ship detection and tracking. The proposed tracking scheme is based on the characteristics of both sea and ship, which include: background information and local position of the ship. We used Morphological "open" operation and frame-subtraction method for background subtraction. The ships are located using the edge information along morphological "thicken", "dilate" and "bridge" operations. The experimental results demonstrate robust and real-time ship detection and tracking over thousands of image frames, and robustness against cluttered background. The detection rate achieved was 98.4%.

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“…In practice, they have a hard time dealing with the large quantity and high variability of the dynamic background objects, and with the limited object information present in infrared sequences [3]. Optical flow [4,5] and block-matching [6] methods have been used with more success. Also, the system needs real-time performance to be useful, which caused us to prefer algorithms operating at the pixel level.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Pixel-Based Methods for the Detection of Floating Objects on the Sea Surface

Borghgraef

Barnich

Lapierre

et al. 2010

EURASIP J. Adv. Signal Process.

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Ship-based automatic detection of small floating objects on an agitated sea surface remains a hard problem. Our main concern is the detection of floating mines, which proved a real threat to shipping in confined waterways during the first Gulf War, but applications include salvaging, search-and-rescue operation, perimeter, or harbour defense. Detection in infrared (IR) is challenging because a rough sea is seen as a dynamic background of moving objects with size order, shape, and temperature similar to those of the floating mine. In this paper we have applied a selection of background subtraction algorithms to the problem, and we show that the recent algorithms such as ViBe and behaviour subtraction, which take into account spatial and temporal correlations within the dynamic scene, significantly outperform the more conventional parametric techniques, with only little prior assumptions about the physical properties of the scene. 2 EURASIP Journal on Advances in Signal Processing www.awm.gov.au 304925

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Motion Projection for Floating Object Detection

Cited by 7 publications

References 7 publications

GPU Based Real-time Floating Object Detection System

GPU Based Real-time Floating Object Detection System

An adaptive moving ship detection and tracking based on edge information and morphological operations

An Evaluation of Pixel-Based Methods for the Detection of Floating Objects on the Sea Surface

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