Protractor: A Fast and Accurate Gesture Recognizer
Yang Li
Google Research
1600 Amphitheatre Parkway
Mountain View, CA 94043
yangli@acm.org
Howdy!
This blog post is a short summary of the above mentioned paper. It also contains my views on the Protractor system proposed in this paper.
Protractor is a gesture recognizer that can be easily implemented and customized for different users. It is very fast and the customization doesn't take a very long time either. Protractor uses the idea of nearest neighbors based on the cosine similarity between two gestures.
The users can create their own gestures and provide samples for them. When the user makes a gesture, that gesture is compared with stored templates and for each stored template, a cosine similarity score is calculated using the following formula:
Due to some orientation adjustment done in the pre- processing step, there is some noise introduced. To overcome this, the input gesture is rotated by an angle as follows:
and 'a' is the dot- product of 'vt' and 'vg'.
The Protractor system performs almost as good as the $1- algorithm but when the number of distinct gestures increases, the performance of Protractor is less affected than that of $1- algorithm.
In my opinion, Protractor serves as a very good tool to recognize gestures for systems that are intended for personal use in the sense that every user has their own system. It is very easy to add custom gestures, something that users would prefer to having a fixed set of gestures.
I used the following sources for this blog post:
[1] Proceeding
CHI '10 Proceedings of the SIGCHI Conference on Human Factors in Computing
Systems. Pages 2169-2172. ACM New York, NY, USA ©2010
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