Computer Vision with OpenCV

7.

Video Analysis and Object Tracking

7.1.

Background Subtraction

7.1.1.

Background Modeling Concepts

7.1.2.

MOG2 Background Subtractor

7.1.2.1.

Gaussian Mixture Models

7.1.2.2.

7.1.2.3.

Shadow Detection

7.1.3.

KNN Background Subtractor

7.1.3.1.

K-Nearest Neighbors Approach

7.1.3.2.

Distance Threshold

7.1.4.

Foreground Mask Processing

7.2.

7.2.1.

Optical Flow Theory

7.2.2.

Lucas-Kanade Method

7.2.2.1.

Sparse Optical Flow

7.2.2.2.

Feature Point Tracking

7.2.2.3.

Pyramidal Implementation

7.2.3.

Dense Optical Flow

7.2.3.1.

Farneback Algorithm

7.2.3.2.

Flow Field Visualization

7.2.3.3.

Motion Analysis

7.3.

Object Tracking

7.3.1.

Single Object Tracking

7.3.2.

MeanShift Tracking

7.3.2.1.

Probability Distribution

7.3.2.2.

Kernel Selection

7.3.3.

CamShift Tracking

7.3.3.1.

Adaptive Window Size

7.3.3.2.

Object Scale Changes

7.3.4.

Modern Tracking Algorithms

7.3.4.1.

BOOSTING Tracker

7.3.4.2.

7.3.4.3.

7.3.4.4.

7.3.4.5.

MEDIANFLOW Tracker

7.3.4.6.

7.3.4.7.

7.3.5.

Tracker Initialization

7.3.6.

Tracker Update Process

7.3.7.

Performance Evaluation

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6. Object Detection and Recognition

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8. Advanced Image Processing