Abstract
In recent years, with the development of the marine industry, the ship navigation environment has become more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count sailing ships to ensure maritime security and facilitate management for Smart Ocean systems. Aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF). The proposed method mainly includes: (1) A self-selective model with a negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of the classifier at the same time; (2) a bounding box regression method combined with a key points matching method for the scale prediction, leading to a fast and efficient calculation. The experimental results show that the proposed method can effectively deal with the problem of ship size changes and background interference. The success rates and precisions were over 8 % higher than Discriminative Scale Space Tracking (DSST) on the marine traffic dataset of our laboratory. In terms of processing speed, the proposed method is higher than DSST by nearly 22 frames per second (FPS).
Highlights
The ocean is rich in resources, which will provide tremendous amounts of materials to address the problem of humans’ resource shortages
In this article, aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF) for smart ocean systems
In order to solve these problems, a new method of mining positive and negative samples based on local regions is proposed which reduces the complexity of a single model and improves the classification ability of the model
Summary
The core issue of target tracking in smart ocean systems is how to select an effective feature extraction method for different scenes to represent the target ship. In this article, aiming at the scaling problem and boundary effect problem of traditional correlation filtering methods, we propose a self-selective correlation filtering method based on box regression (BRCF) for smart ocean systems. The method mainly includes: (1) A hard negative sample mining method to reduce the boundary effect of the correlation filter; (2) a self-selective model with adaptive multi-response fusion method to strengthen the classification capability of the tracking model; (3) a key points matching method for scale pre-estimation of the tracking target; (4) a bounding box regression method to adjust the bounding box of the tracking target freely.
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