https://github.com/ndrplz/surround_vehicles_awareness

Learn to map surrounding vehicles onto a bird's eye view of the scene.
https://github.com/ndrplz/surround_vehicles_awareness

adas bird-eye deep-learning self-driving-car synthetic-data

Last synced: 3 months ago
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Learn to map surrounding vehicles onto a bird's eye view of the scene.

• Host: GitHub
• URL: https://github.com/ndrplz/surround_vehicles_awareness
• Owner: ndrplz
• License: mit
• Created: 2017-04-21T14:41:09.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2020-02-12T02:00:38.000Z (over 5 years ago)
• Last Synced: 2023-11-07T18:24:40.037Z (almost 2 years ago)
• Topics: adas, bird-eye, deep-learning, self-driving-car, synthetic-data
• Language: Python
• Homepage:
• Size: 6.12 MB
• Stars: 189
• Watchers: 13
• Forks: 69
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Learning to Map Vehicles into Bird's Eye View



  



This code accompanies the paper *["Learning to map surrounding vehicles into bird's eye view using synthetic data"](https://arxiv.org/pdf/1706.08442.pdf)*.

It contains the code for loading data and pre-trained SDPN model proposed in the paper.

## How-to-run

Script entry-point is in **[main.py](main.py)**. 

When **[main.py](main.py)** is run, *pretrained weights* are automatically downloaded and injected in the **[model](model.py)**.

Model is then used to perform and inference on a sample data, mapping a car from the dashboard camera view to the bird's eye view of the scene. If everything works correctly, the output should look like this.



  



#### Dependencies

The code was developed with the following configuration:

* python 2.7.11

* numpy 1.11.2

* opencv 3.1.0

* Theano 0.9.0.dev3

* Keras 1.1.2

Other configuration will reasonably work, but have never been explicitly tested.

## Dataset 

In this repository only one example is provided, to the end of verifying that the model is working correctly.

The **whole dataset**, which comprises more than **1M** couples of bounding boxes, can be found here.

To get an idea of how the data look like you can check [this video](https://www.youtube.com/watch?v=t2mXv9j6LNw).