A Self-Selective Correlation Ship Tracking Method for Smart Ocean Systems

Song, Bin; Guo, Jie; Du, Xiaojiang; Guizani, Mohsen

doi:10.3390/s19040821

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…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. Zhang et al [25] proposed a discrete cosine transformation-based ship detection framework to obtain ship trajectories from maritime images shot by non-stationary platform cameras.…”

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 network pre-computes Siamese subnetwork's template branches and performs online tracking by representing the relevant layers as mundane convolutional layers, thus improving tracking accuracy and speed. Xu et al [12] proposed a self-selective correlation filtering method based on box regression (BRCF) to address the scaling problem and boundary effect problem of traditional correlation filtering methods. The experimental results show that the proposed method can effectively deal with the problem of ship size change and background interference, and improve the accuracy of ship tracking.…”

Section: Discriminative Methodsmentioning

confidence: 99%

A lightweight multi-target ship tracking model based on Yolov7

Cen,

Chen,

Liu

et al. 2024

Phys. Scr.

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Ship multi-target tracking has long been a topic that has attracted researchers from different fields. Currently, the following challenges still exist in ship multi-target tracking. Ships obscuring each other leads to an increase in misdetections and omissions in the algorithm. Some algorithms have a large number of parameters and computations, which are not favourable for deployment into devices. As a result, we present a novel multi-target tracking technique that combines the Yolov7 detector with Kalman filtering. First, the retrieval of fine ship details in the video is accomplished by employing the CNNS(Convolutional Neural Networks) + Transformer + CNNS architecture. Second, we introduce a novel lightweight module known as Light-SPP, which aims to integrate ship features. Finally, Wise-iou Loss, which increases the predictability of ship position, is shown as the detector's localization loss function. The experimental results show that the number of parameters and computation of the model decrease by 11.0% and 17.7% respectively, and the continuous tracking accuracy and tracking and positioning accuracy improve by 17.7% and 7.4% respectively. In summary, our proposed ship multi-target tracking algorithm mainly solves the problems of low tracking accuracy and large computational volume, and has high engineering application prospects in the field of water transport.

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“…During the decision-making process, plant areas were successfully classified with a success rate of 92%. Kang et al [8] proposed a self-selective correlation filtering method based on frame regression (BRCF), which can effectively deal with the problem of ship size change and background interference and the success rate and accuracy on the maritime traffic data set More than 8 percentage points higher than Discriminative Scale Space Tracking (DSST). Tang et al [9] applied the gradient-weighted histogram of the directional gradient algorithm to the global texture feature to extract the vector, and obtained the improved HOG feature.…”

Section: Methods Based On Traditional Image Processingmentioning

confidence: 99%

TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

Zhao

Sun

et al. 2023

Drones

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Drone detection is a significant research topic due to the potential security threats posed by the misuse of drones in both civilian and military domains. However, traditional drone detection methods are challenged by the drastic scale changes and complex ambiguity during drone flight, and it is difficult to detect small target drones quickly and efficiently. We propose an information-enhanced model based on improved YOLOv5 (TGC-YOLOv5) for fast and accurate detection of small target drones in complex environments. The main contributions of this paper are as follows: First, the Transformer encoder module is incorporated into YOLOv5 to augment attention toward the regions of interest. Second, the Global Attention Mechanism (GAM) is embraced to mitigate information diffusion among distinct layers and amplify the global cross-dimensional interaction features. Finally, the Coordinate Attention Mechanism (CA) is incorporated into the bottleneck part of C3, enhancing the extraction capability of local information for small targets. To enhance and verify the robustness and generalization of the model, a small target drone dataset (SUAV-DATA) is constructed in all-weather, multi-scenario, and complex environments. The experimental results show that based on the SUAV-DATA dataset, the AP value of TGC-YOLOv5 reaches 0.848, which is 2.5% higher than the original YOLOv5, and the Recall value of TGC-YOLOv5 reaches 0.823, which is a 3.8% improvement over the original YOLOv5. The robustness of our proposed model is also verified on the Real-World open-source image dataset, achieving the best accuracy in light, fog, stain, and saturation pollution images. The findings and methods of this paper have important significance and value for improving the efficiency and precision of drone detection.

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A Self-Selective Correlation Ship Tracking Method for Smart Ocean Systems

Cited by 9 publications

References 42 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

A lightweight multi-target ship tracking model based on Yolov7

TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

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