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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
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Passive Filters01:27

Passive Filters

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Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
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Active Filters01:25

Active Filters

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Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
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Correlation01:09

Correlation

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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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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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STAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filters.

Ruey-Kai Sheu1, Mayuresh Pardeshi2, Lun-Chi Chen3

  • 1Department of Computer Science, Tunghai University, Taichung 40704, Taiwan.

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

This study introduces a novel suspicious tracking mechanism across multiple cameras (STAM-CCF) using correlation filters. It effectively tracks and re-identifies suspicious individuals in real-time, even newcomers, across various surveillance scenarios.

Keywords:
feature based trackingmulti-camera trackingsurveillancesuspicious tracking

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computer Vision

Background:

  • Intelligent video surveillance demands real-time suspicious behavior tracking across multiple cameras in public areas.
  • Existing methods struggle with newcomer recognition and multi-camera feature extraction, especially with occlusions and overlapping objects.
  • Learning-based and feature-based approaches have limitations in recognizing unknown individuals and handling complex multi-camera environments.

Purpose of the Study:

  • To design and implement a robust suspicious tracking mechanism across multiple cameras (STAM-CCF) utilizing correlation filters.
  • To enhance human identification and mitigate tracking errors caused by occlusion and overlapping objects within camera views.
  • To address the challenge of re-identifying individuals across multiple cameras using a novel camera correlation model and a two-stage gait recognition strategy.

Main Methods:

  • Leveraging camera geographical information and the YOLO object detection framework for improved human identification.
  • Implementing a camera correlation model to facilitate re-identification across different camera views.
  • Employing a two-stage gait recognition strategy to accurately re-identify individuals in multi-camera surveillance systems.

Main Results:

  • The proposed STAM-CCF method demonstrates high accuracy in suspicious behavior tracking and re-identification.
  • Experimental results confirm the system's effectiveness in continuous within-camera tracking of suspicious activities.
  • Successful re-identification of suspicious individuals across multiple cameras was achieved, validating the method's performance.

Conclusions:

  • STAM-CCF provides a highly accurate and reliable solution for real-time suspicious tracking in multi-camera intelligent surveillance systems.
  • The method effectively handles challenges like newcomer recognition, occlusions, and re-identification across cameras.
  • This approach significantly advances the capabilities of public area surveillance for enhanced security.