Tag: project

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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Boise Bicycle Project

BBP is considered an “essential” business under the Governor’s Stay at Home Order as a social service and transportation organization. Below are the essential services being offered.

Social Services

Free Bicycle Repair: If your bicycle is your only/primary source of transportation, BBP is providing up to 30 minutes repair services at no cost. This service is intended for those without the ability to pay. Knock on the door Wed-Saturday 11-5pm and we will get you rolling.

Nonprofit Bike Repair: Partnering nonprofits can drop off bicycles to be repaired for their clients. If you are a nonprofit and have someone/s who needs their bike fixed, please call 208-429-6520 or email info@boisebicycleproject.org. Adult and teen referrals are receiving priority repair services.

Bikes for Nonprofits: Partnering nonprofits can request bicycles for their clients in urgent need. BBP will work hard to provide these bicycles within 1-5 days of request. If you are a

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GitHub – balancap/SDC-Vehicle-Detection: Udacity Project

The goad of this project is to implement a robust pipeline capable of detecting moving vehicles in real-time. Even though the project was designed for using classic Computer Vision techniques, namely HOG features and SVM classifier, in agreement the course organizers, I decided like a few other students to go for a deep learning approach.

Several important papers on object detection using deep convolutional networks have been published the last few years. More specifically, Faster R-CNN, YOLO and Single Shot MultiBox Detector are the present state-of-the-art in using CNN for real-time object detection.

Even though there are a few differences between the three previous approaches, they share the same general pipeline. Namely, the detection network is designed based on the following rules:

  • Use a deep convolutional network trained on ImageNet as a multi-scale source of features. Typically, VGG, ResNet or Inception;
  • Provide a collection of pre-defined anchors boxes tiling the
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KSU Physics Education Bike Project

KSU Physics Education Bike Project

Scientific and Cultural Aspects of the Bicycle:

An International Pedagogical Project


 


This project is a multi-national effort to collaborate on the adaptation
and creation of pedagogical materials.  The bicycle, a highly developed
yet simple device, is the focus of this effort.  Students and faculty
are using materials developed in a variety of countries and creating new
materials using contemporary multimedia.  This effort began almost
15 years ago when Robert Fuller and Dean Zollman created the videodisc
Energy
Transformations featuring the Bicycle
at about the same time that the
PLON Project in The Netherlands developed the teaching module Traffic
and the British Open University developed a course on Materials and Structures
which featured the bicycle.  These efforts were independent of each
other.  Since that time we have worked to combine instructional materials
from these and other countries.
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udacity/self-driving-car: The Udacity open source self-driving car project

Self-Driving Car

We’re Building an Open Source Self-Driving Car

And we want your help!

At Udacity, we believe in democratizing education. How can we provide opportunity to everyone on the planet? We also believe in teaching really amazing and useful subject matter. When we decided to build the Self-Driving Car Nanodegree program, to teach the world to build autonomous vehicles, we instantly knew we had to tackle our own self-driving car too.

Together with Google Self-Driving Car founder and Udacity President Sebastian Thrun, we formed our core Self-Driving Car Team. One of the first decisions we made? Open source code, written by hundreds of students from across the globe!

You can read more about our plans for this project.

Contributions

Here’s a list of the projects we’ve open sourced:

How to Contribute

Like any open source project, this code base will require a certain amount of thoughtfulness. However, when you add a

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ahmetozlu/vehicle_counting_tensorflow: “MORE THAN VEHICLE COUNTING!” This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API.

This sample project focuses on “Vechicle Detection, Tracking and Counting” using TensorFlow Object Counting API.


The TensorFlow Object Counting API is used as a base for object counting on this project, more info can be found on this repo.



The developing is on progress! This sample project will be updated soon, the more talented traffic analyzer app will be available in this repo!


General Capabilities of This Sample Project

This sample project has more than just counting vehicles, here are the additional capabilities of it:

  • Detection and classification of the vehicles (car, truck, bicycle, motorcycle, bus)
  • Recognition of approximate vehicle color
  • Detection of vehicle direction of travel
  • Prediction the speed of the vehicle
  • Prediction of approximate vehicle size
  • The images of detected vehicles are cropped from video frame and they are saved as new images under “detected_vehicles” folder path
  • The program gives a .csv file as an output (traffic_measurement.csv)
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