Vehicle Detection and Tracking
Identification and tracking of vehicles.
conda env create -f environment.yml source activate environment
Usage: video_pipeline.py [-i <file>] [-o <file>] video_pipeline.py -h | --help Options: -h --help -i <file> --input <file> Input text file [default: ../videos/project_video.mp4] -o <file> --output <file> Output generated file [default: ../videos/project_video_output.mp4]
- Input video needs to be a feed from centered onboard camera.
- You will need to download the training data (vehicle and non-vehicle images) and put in a folder named
data. It has been ignored for sizing issues.
python video_pipeline.py-i ../videos/project_video.mp4 -o ../videos/project_video_output.mp4
In this project, my main goal is to write a software pipeline to identify and track vehicles in a video from a front-facing camera on a car (without any false positives).
You will find the exploration code for this project is in the IPython Notebook and a video displaying how my pipeline can allow to detect and track vehicles on the road. A more detailed report of the project is available here.
The steps of this project are the following:
- Build a feature engineering pipeline to create a dependent variable for our classification model.
- Train a classifier using the selected features (HOG features and color features in our case).
- Implement a sliding window search and use the trained classifier to search for vehicles in images
- Create a video pipeline that create bounding boxes and identify vehicles most of the time with minimal false positives.