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Category: Machine Learning
Allgemein Machine Learning
[Kaggle] Minority Report, or the San Francisco Random Forest Precog

I had a little free time on my hand and decided to quickly complete the coursera-course „Data Science at Scale – Practical predictive analytics“ of the University of Washington by Bill Howe. The last assignment was to participate in a kaggle competition.

Source: https://commons.wikimedia.org/wiki/File:ExpoSYFY_-_Minority_Report_(10825723756).jpg
Source: https://commons.wikimedia.org/wiki/File:ExpoSYFY_-_Minority_Report_(10825723756).jpg

For this assignment I chose the „San Francisco Crime Classification“ challenge. The task is to predict the Category of a crime given the time and location. The dataset contains incidents from the SFPD Crime Incident Reporting system from 2003 to 2015 (878049 datapoints for training) with the following variables:

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TobiasWeis | 30. August 2016
Blender Computer Vision Machine Learning Short Simulation
Groundtruth data for Computer Vision with Blender

In the video below you can see the sequence of a car driving in a city scene and braking. The layers I rendered out for groundtruth data are the rendered image with the boundingbox of the car (top left), the emission layer ( shows the brakelights when they start to emit light, top right ), the optical flow (lower left), and the depth of each pixel in the world scene ( lower right).


Render-time was about 10h on a Nvidia GeForce GTX 680, tilesize 256×256, total image-size: 960×720. In this article I will first demonstrate how to set up the depth rendering, and afterwards how to extract, save and recover the optical flow.

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TobiasWeis | 14. Januar 2016
Computer Vision Feature Representations Machine Learning Robotics Simulation
Displacement priors

What is the target of all this ? Driving in an automotive scenario with a given speed and turnrate at any moment, we want to predict the displacement of a 2D-projection (pixel) between two frames:
p(\vec{uv}_{x,y} | speed, turnrate, camera-matrix, world-geometry)

By using the camera-calibration, I can create artificial curves and walls as 3D point-sets and project them back to 2D. Using discretized values for speed, turnrate, streetwidth and wall-height, I can then simulate the displacement of these 3D-Points when they are projected to 2D (our image).
(Note for me: this is the backprojection-code, main-file: main_displacements.py)

2_flows

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TobiasWeis | 16. Oktober 2015