Tag: detection

GitHub – udacity/CarND-Vehicle-Detection: Vehicle Detection Project

Udacity - Self-Driving Car NanoDegree

In this project, your 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. Check out the writeup template for this project and use it as a starting point for creating your own writeup.

Creating a great writeup:

A great writeup should include the rubric points as well as your description of how you addressed each point. You should include a detailed description of the code used in each step (with line-number references and code snippets where necessary), and links to other supporting documents or external references. You should include images in your writeup to demonstrate how your code works with examples.

All that said, please be concise! We’re not looking for you to write a book here, just

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JunshengFu/vehicle-detection: Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).

Objective

A demo of Vehicle Detection System: a monocular camera is used for detecting vehicles.

(1) Highway Drive (with Lane Departure Warning) (Click to see the full video)

gif_demo1

(2) City Drive (Vehicle Detection only) (Click to see the full video)

gif_demo2


Code & Files

1. My project includes the following files


Others are the same as in the repository of Lane Departure Warning System:

  • calibration.py contains the script to calibrate camera and save the calibration results
  • lane.py contains the lane class
  • examples folder contains the sample images and videos

2. Dependencies & my environment

Anaconda is used for managing my dependencies.

  • You can use provided environment-gpu.yml to install the dependencies.
  • OpenCV3, Python3.5, tensorflow, CUDA8
  • OS: Ubuntu 16.04

3. How to run the code

(1) Download weights for YOLO

You can download the weight from here and save it to
the weights folder.

(2) If you want to run the demo,

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