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PROJECT
Pedestrian Detection Using Histograms of Oriented Gradients (HOG)
Learn how to implement a machine learning pedestrian detection for video analysis. Pedestrian detection is fundamental in any intelligent video surveillance system because it's the foundation for more advanced semantic analysis of video streams.
You will learn to:
Plot graphs using Python
Extract and process frames from a video using OpenCV
Understand the HOG pedestrian detector
Identify which frames have pedestrians using matplotlib
Skills
Data Visualization
Data Manipulation
Machine Learning Fundamentals
Prerequisites
Basic understanding of Python
Basic understanding of image manipulation
Basic understanding of data visualization
Technologies
Python
OpenCV
Project Description
Pedestrian detection is one of the most fundamental aspects of computer vision.
To understand how a Histogram of Oriented Gradients (HOG) works, we must first know what a gradient and a histogram are.
The gradient measures how rapidly and in which direction the level varies in a black and white image. The grayscale level is similar to height in a black and white image. A substantial gradient from white to black perpendicular to a picture edge (a black to white transition) results from a strong gradient perpendicular to the edge.
For each pixel, the system computes a gradient, and the gradients are used to fill a histogram: the value is the gradient’s angle, and the weight is the gradient’s magnitude. To determine whether the cells in the current detection window match a person or not, the system combines the histograms of all cells and sends them to a machine learning discriminator.
This technique has been designed to identify pedestrians who are completely visible and standing up. As a result, we should not expect it to function in other situations.
Project Tasks
1
Histograms of Oriented Gradients
Task 1: Import Necessary Libraries
Task 2: Plot the Histograms
Task 3: Plot Weighted Histograms
Task 4: Preparing an Array to Demonstrate HOG
Task 5: Calculate Histograms of Oriented Gradients (HOG)
Task 6: Plot the Norm and Show the Magnitude
Task 7: Plot HOG Using pyplot
2
Working With Single Frame of Video
Task 8: Load Detector
Task 9: Load the Video and Read the Frame
Task 10: Detect a Pedestrian
Task 11: Label and Visualize the Image with Detected Boxes
3
Working With the Whole Video
Task 12: Loop Over Previous Steps
Congratulations!