By the end of this course, learners will be able to analyze video data, apply color models, implement image preprocessing techniques, and build object detection and tracking solutions using OpenCV and Python. They will gain the ability to process real-time and recorded video streams, extract meaningful visual features, and apply motion analysis algorithms to solve practical computer vision problems.
Analyze Video Data Using OpenCV and Python
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
8 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Analyze video streams and apply image preprocessing techniques using OpenCV and Python.
Implement object detection, tracking, and motion analysis for real-world video data
Apply color models, contours, and optical flow to extract meaningful visual features.
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
February 2026
Assessments
16 assignments
Taught in English
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There are 4 modules in this course
This module introduces the fundamentals of video analytics using OpenCV, focusing on how visual data is represented and processed. Learners explore core concepts of video analytics, understand how different color models influence image interpretation, and gain hands-on insight into image loading and basic preprocessing. The module establishes a strong conceptual foundation required for effective computer vision workflows.
What's included
7 videos4 assignments
This module focuses on essential image segmentation techniques and the OpenCV framework. Learners study thresholding methods for separating objects from backgrounds, explore OpenCV’s architecture and performance advantages, and understand how object detection integrates into tracking pipelines for real-time video analysis.
What's included
7 videos4 assignments
This module covers practical aspects of working with video streams and mid-level feature detection. Learners gain skills in capturing and saving video data, explore blob detection for identifying regions of interest, and apply color-based tracking techniques to follow objects in dynamic scenes.
What's included
7 videos4 assignments
This advanced module introduces motion analysis and sophisticated tracking algorithms. Learners explore smoothing and contour detection for shape analysis, apply adaptive tracking algorithms such as CamShift, and implement optical flow and face detection techniques to handle complex real-world video scenarios.
What's included
8 videos4 assignments
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Frequently asked questions
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