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You will learn to:
Process videos in Python.
Identify moving objects in videos.
Track moving objects in videos.
Obtain statistics about moving objects in videos.
Skills
Computer Vision
Machine Learning
Prerequisites
Intermediate understanding of Python
Basic understanding of object detection
Familiarity with OpenCV
Technologies
Python
OpenCV
Project Description
Analyzing videos manually to extract basic statistics requires significant human resources and time. Computer vision helps automate this process in many areas. It can be applied to almost anything, from counting birds in the sky to identifying foods in images.
In this project, we’ll learn how to detect and track objects. We’ll build a Python application and use the popular OpenCV library. We’ll use footage of traffic at an intersection to count the cars entering and leaving. We can also apply these CV techniques to other problems in this field, so feel free to experiment afterward.
This project uses OpenCV, Tkinter, NumPy, and pandas. We’ll extend our knowledge in computer vision with OpenCV, learn GUI interaction with Tkinter, and perform mathematical computations and data operations using NumPy and pandas.
Project Tasks
1
Object Detection and Tracking
Task 0: Get Started
Task 1: Import the Necessary Modules
Task 2: Detect Objects in a Video
Task 3: Compute the Delta Frame
Task 4: Perform Thresholding and Dilation
Task 5: Detect Contours and Mark Moving Objects
Task 6: Mark the Centroids of Each Contour
2
Car Counting and Fine-Tuning
Task 7: Create a Line Class
Task 8: Draw Lines on the Canvas
Task 9: Implement the Car Tracker
Task 10: Track Cars
Task 11: Fine-Tune the Code to Report Statistics
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.