Training and objects recognition

object recognition training

object recognition training

Conclusively there are two ways of objects recognition, which are

  • combining simple shapes’ recognition to detect complicated objects;
  • using pre-trained data to match objects if they are as set.

The ways we used to detect circles and lines by openCV belong to the first category, and training needed recognition ways such as faces detection and hands detection belong to the second category. Recognition can be quite stable after good data training, but it may not recognize other features without pre-defined, so does the rules of faces detection with openCV.

But if recognition is used to design other applications such as objects controlling or multi-touch demonstrations, it could not be better choice adopting training way to set up recognition functionalities. As the picture shown above, i abstract the recognition part from ARtoolkit which is a popular open source library in internet. The training and recognition test results shows that  it can recognize nearly all the patterns pre-trained.

Author: Andol Li

A HCI researcher, a digital media lecturer, an information product designer, and a python/php/java coder.

3 Comments On “ Training and objects recognition”

  1. Hi Andol, you have done really a superb work. Though I am very late to Augmented Reality but I have started working for it.Will you please provide OpenCV snippet for Augmented reality app ?

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