Vessel Detection and Tracking Method Based on Video Surveillance

Wawrzyniak, Natalia; Hyla, Tomasz; Popik, Adrian

doi:10.3390/s19235230

Cited by 32 publications

(24 citation statements)

References 32 publications

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“…Park et al [22] proposed a passive ship tracking model to accurately extract ship trajectories from maritime videos shot by onboard monocular camera. Wawrzyniak et al [23] integrated the background subtraction and bounding box methods to track ships in maritime video streams which was demonstrated with typical ship tracking challenges. Kang et al [24] proposed a self-selective correlation filtering method to track ships by tackling the challenges of ship imaging size variation and background interference.…”

Section: Introductionmentioning

confidence: 99%

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Chen

et al. 2020

Sensors

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Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker’s performance on typical maritime traffic scenarios through four maritime surveillance videos.

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Section: Introductionmentioning

confidence: 99%

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Chen

et al. 2020

Sensors

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“…The proposed method has as one of its components a vessel detection and tracking method proposed in [15]. The detection method takes as its input video streams and returns a series of pictures with the same tracking ID.…”

Section: Contributionmentioning

confidence: 99%

“…The detection method takes as its input video streams and returns a series of pictures with the same tracking ID. The main new contribution compared with [15] is the determination of the vessel ID for the tracking ID.…”

Section: Contributionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Vessels on Inland Waters Using Low-Quality Video Streams

Hyla¹,

Wawrzyniak²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Video surveillance can be used to monitor recreational and commercial vessels on inland waters. It is difficult to identify vessels on the basis of registration numbers alone, especially when structure and size are not standardized in a way that allows for easy recognition. In this paper, we propose a novel vessel identification method that can work continuously on streams with a high compression rate or visible compression artifacts. This method can identify all types of moving vessels during daylight. It should work with existing video monitoring systems used to monitor inland waters. When a vessel has hull inscriptions, the method can identify that vessel. If hull inscriptions are not present, the method identifies the direction of movement of the vessel. The tests were performed by real-time video playback of prerecorded samples in the test environment. The method correctly identifies 94% of vessels and the performance indicates that it is suitable for practical application.

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“…Wawrzyniak et al [ 6 ] present a method that allows for the detection and tracking of ships using the video streams of existing monitoring systems for ports and rivers. The article presents the way and results of experiments conducted on selected groups of data using selected cameras with different working parameters.…”

Section: Overview Of Contributionsmentioning

confidence: 99%