Check out this link for the final result -> https://youtu.be/2dapyq_oO2Q
In this project, the goal is to write a software pipeline to detect vehicles in a video (start with the test_video.mp4 and later implement on full project_video.mp4), but the main output or product we want you to create is a detailed writeup of the project.
The goals / steps of this project are the following:
- Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
- Applied a color transform and append binned color features, as well as histograms of color, to the HOG feature vector.
- Note: Normalize the features and randomize a selection for training and testing.
- Implement a sliding-window technique and use a trained classifier to search for vehicles in images.
- Run the pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.
- Estimate a bounding box for vehicles detected.
Here are links to the labeled data for vehicle and non-vehicle examples to train the classifier. These example images come from a combination of the GTI vehicle image database, the KITTI vision benchmark suite, and examples extracted from the project video itself. You are welcome and encouraged to take advantage of the recently released Udacity labeled dataset to augment the training data.
Some example images for testing the pipeline on single frames are located in the test_images folder. The video called project_video.mp4 is the video the pipeline should work well on.