# Self-Selective Correlation Ship Tracking Method for Smart Ocean System

## TL;DR

This paper introduces a novel self-selective correlation filtering method with box regression for ship tracking in complex marine environments, improving accuracy and speed over existing methods.

## Contribution

The paper presents a new correlation filtering approach with negative sample mining and bounding box regression, addressing boundary effects and scale prediction in maritime ship tracking.

## Key findings

- Achieved over 8% higher success rates and precision than DSST.
- Increased processing speed by nearly 22 FPS compared to DSST.
- Effectively handles ship size changes and background interference.

## Abstract

In recent years, with the development of the marine industry, navigation environment becomes more complicated. Some artificial intelligence technologies, such as computer vision, can recognize, track and count the sailing ships to ensure the maritime security and facilitates the management for Smart Ocean System. 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 include: 1) A self-selective model with negative samples mining method which effectively reduces the boundary effect in strengthening the classification ability of 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 higher than Discriminative Scale Space Tracking (DSST) by over 8 percentage points 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).

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Source: https://tomesphere.com/paper/1902.09690