Welcome to my site

My name is Ignacio Mellado. I am an engineer working on intelligent machines, especially on visual perception and control. Computer Vision and Machine Learning are usual suspects in my lab. In this website you will find a selection of my projects, experiments and random thoughts.

Boosting color-based tracking

Tracking an object over a video sequence is a classic problem in Computer Vision. A tracking algorithm looks for the object at every new frame, given a model of the object. This model holds information [...]

There are tons of tutorials on color-based tracking that use the histogram back-projection to find those pixels in the image that are most likely to be object pixels. Then, they use MeanShift [...]

After normalization, each bin value in the color histogram expresses the probability that a pixel has a color in bin B, given that it is a pixel of object O. We can express this as the conditional probability [...]

The Perceptive Portable Device
Can a smartphone perceive the environment like a human does? Portable devices are full of sensors, but they are still very limited to understand what is happening from a human perspective: Where am I inside the building? Is my user healthy? Is the baby crying? This side project is my quest to give portable devices such capabilities.
Autonomous LinkQuad quadcopter with Computer Vision
MAVwork, my open-source framework for visual control of multirotors, is now supporting a new quadcopter from UAS Technologies Sweden. Everything was tested with a speed control application. Watch a semiautonomous flight of this new elegant drone.
Autonomous Pelican quadcopter with Computer Vision
Check how the versatile Pelican from Ascending Technologies acquired basic automatic take-off, hover and landing capabilities thanks to MAVwork, the open-source framework for drone control. Watch the open MultirotorController4mavwork in action.
Camera localization with visual markers
There are tons of applications where it is key to have the accurate location of things in a workspace. With these cheap and easy-to-build visual markers, you can know the position and attitude of anything with a camera on it. They block less visual space and offer less air resistance than equivalent-size 2D codes.
MAVwork released for Parrot AR.Drone
MAVwork is a framework for drone control that was born in 2011 during a short research stay in the Australian Research Centre for Aerospace Automation (ARCAA). Read about the inception of MAVwork and watch a video of the first test controller for a Parrot AR.Drone with a Vicon system.
Laura: Self-driving public transportation. Prototype II.
Discover how this 12-ton truck was automated to drive itself with Computer Vision. This was the second prototype, after a Citroën C3, in a project led by Siemens to develop a self-driving public transportation system.
Laura: Self-driving public transportation. Prototype I.
High buildings blocking GPS signal, lane markings and road signs hidden by traffic, ... Cities can be a very harsh environment for a driverless bus trying to know where it is and where to go. In this project, led by Siemens, I explored a solution with Computer Vision.