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Related Concept Videos

Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
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Correlation01:09

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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What is Natural Selection?01:32

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Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.
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Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Coefficient of Correlation01:12

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
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A Self-Selective Correlation Ship Tracking Method for Smart Ocean Systems.

Xu Kang1, Bin Song2, Jie Guo3

  • 1The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China. xkang0591@gmail.com.

Sensors (Basel, Switzerland)
|February 21, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new AI method for tracking ships in complex marine environments. The self-selective correlation filtering with box regression (BRCF) improves accuracy and speed for maritime security and Smart Ocean systems.

Keywords:
box regressioncorrelation filternegative samples miningself-selective modelship tracking

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Marine Technology

Background:

  • The marine industry's growth complicates ship navigation environments.
  • Existing computer vision methods face challenges with ship scaling and boundary effects.
  • Smart Ocean systems require robust ship recognition and tracking for security and management.

Purpose of the Study:

  • To address the scaling and boundary effect problems in traditional correlation filtering for ship tracking.
  • To propose a novel self-selective correlation filtering method based on box regression (BRCF).
  • To enhance the accuracy and efficiency of ship detection and tracking in complex maritime settings.

Main Methods:

  • Developed a self-selective model incorporating negative sample mining to reduce boundary effects and improve classification.
  • Integrated a bounding box regression method with key point matching for efficient scale prediction.
  • Utilized a marine traffic dataset for experimental evaluation.

Main Results:

  • The proposed BRCF method effectively handles variations in ship size and background interference.
  • Achieved over 8% higher success rates and precision compared to Discriminative Scale Space Tracking (DSST).
  • Demonstrated a processing speed improvement of nearly 22 frames per second over DSST.

Conclusions:

  • BRCF offers a significant advancement in AI-powered ship tracking for maritime applications.
  • The method enhances both the accuracy and computational efficiency of ship monitoring systems.
  • BRCF contributes to improved maritime security and management within Smart Ocean systems.