Robust Ship Tracking via Multi-view Learning and Sparse Representation

Chen, Xinqiang; Wang, Shengzheng; Shi, Chaojian; Wu, Huafeng; Zhao, Jiansen; Fu, Junjie

doi:10.1017/s0373463318000504

Cited by 101 publications

(74 citation statements)

References 47 publications

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“…The histogram of oriented gradient (HOG), local binary pattern. (LBP), and Gabor feature descriptors are commonly used to identify target contours for the purpose of helping varied trackers obtain high tracking accuracy which indeed have enjoyed huge success in many object tracking applications [10]. The Canny feature descriptor can effectively overcome the edge variation challenge (caused by wave imaging changes) [37], and the LoG descriptor is a popular blob-detector providing a complementary description of ship shape [38].…”

Section: Ship Feature Extractionmentioning

confidence: 99%

“…Previous studies mainly employed automatic identification systems (AIS) to track ships sailing in inland waterways [4][5][6], and several techniques (e.g., synthetic aperture radar (SAR), long-range identification and tracking (LRIT)) have been integrated to further enhance ship tracking accuracy [7][8][9][10]. More specifically, maritime traffic participants can track ship positions with the LRIT technique over large time intervals (usually every six hours) when the ship travels far away from coastal areas.…”

Section: Introductionmentioning

confidence: 99%

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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: Ship Feature Extractionmentioning

confidence: 99%

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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“…Weng et al used the association rules to determine crucial maritime accident contributory factors and mutual coupled effects on the ship detention incident [11]. Similar studies can be found in [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 90%

Mining ship deficiency correlations from historical port state control (PSC) inspection data

Chen

et al. 2020

PLoS ONE

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Early warning on the ship deficiency is crucial for enhancing maritime safety, improving maritime traffic efficiency, reducing ship fuel consumption, etc. Previous studies focused on the ship deficiency exploration by mining the relationships between the ship physical deficiencies and the port state control (PSC) inspection results with statistical models. Less attention was paid to discovering the correlation rules among various parent ship deficiencies and subcategories. To address the issue, we proposed an improved Apriori model to explore the intrinsic mutual correlations among the ship deficiencies from the PSC inspection dataset. Four typical ship property indicators (i.e., ship type, age, deadweight and gross tonnage) were introduced to analyze the correlations for the ship parent deficiency categories and subcategories. The findings of our research can provide basic guidelines for PSC inspections to improve the ship inspection efficiency and maritime safety.

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“…e size of the adhesion coefficient mainly depends on the condition of the road surface and the tire, and the road surface factors mainly include the type of road surface, roughness and dryness, and humidity. e tire factor is mainly the tread pattern of the tire, that is, the type and depth of the tread [22][23][24].…”

Section: Road Factorsmentioning

confidence: 99%

Research of Vehicle Rear-End Collision Model considering Multiple Factors

Luo

Zang

Yuan

et al. 2020

Mathematical Problems in Engineering

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e accuracy of the rear-end collision models is crucial for the early warning of potential traffic accident identification, and thus analyzes of the main factors influencing the rear-end collision relevant models is an active topic in the field. e previous studies have tried to determine the single factor influence on the rear-end collision model performance. Less attention was paid to exploit mutual influences on the model performance. To bridge the gap, we proposed an improved vehicle rear-end collision model by integrating varied factors which influence two parameters (i.e., response time and road adhesion coefficient). e two parameters were solved with the integrated weighting and neural network models, respectively. After that we analyzed the relationship between varied factors and the minimum car-following distance. e research findings support both the theoretical and practical guidance for transportation regulations to release more reasonable minimum headway distance to enhance the roadway traffic safety.On the basis of the traditional safety distance model, Kong et al. conducted information acquisition, distance judgment, braking, and other processes for active collision avoidance during vehicle travel. However, the judgment model used is relatively simple [5]. Jiang and Yu established a highway antitailing model based on radial basis function neural network by using traditional algorithms [6]. Nevertheless, the model is mainly applied to highways, and the influencing factors are considered to be single. Jing and Wang explored the influence of multiple vehicles on the driver and established a vehicle-following model considering the traffic state based on the full velocity difference (FVD) model [7]. Nonetheless, the model is too complicated to conduct to practical applications. Xia established a fuzzy inference system based on the characteristics (e.g., driving age, and gender) of the driver to determine the response time of different drivers and proposed an improved version of the safe model [8]. Peng and Sun derived the modified Korteweg-de Vries (mKdV) equation to describe the traffic

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Robust Ship Tracking via Multi-view Learning and Sparse Representation

Cited by 101 publications

References 47 publications

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

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

Mining ship deficiency correlations from historical port state control (PSC) inspection data

Research of Vehicle Rear-End Collision Model considering Multiple Factors

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