Vehicle Detection
using HOG

Intro

In this section, we will take a look at one such feature extraction technique, the The Histogram of oriented Gradients , which transforms image pixels into a vector representation that is sensitive to broadly informative image features regardless of confounding factors like illumination. We will use these features to develop a simple vehicle detection pipeline, using machine learning algorithms .

Reading all Vehicle images and converting them to Grayscale

Visualization of HOG features

Train Linear SVM

Obtain a set of image thumbnails of Vehicles to constitute "positive" training samples.

Obtain a set of image thumbnails of non-Vehicles to constitute "negative" training samples.

Extract HOG features from these training samples.

Train a linear SVM classifier on these samples.

Test