I was at a demo for Kinnect SDK yesterday. I was thrilled with the technology. Kinnect is a wonderful piece of technology putting together a lot of cut edge technologies and pretty amazing algorithms. The infrared camera does a real magic and picks up a 3D image! A good estimation of depth for each pixel.
If you have ever spent time on image processing and finding blobs or objects using pattern recognition techniques, you know what I mean. Having such an accurate estimation of depth for each pixel can revolutionize any object recognition algorithm.
After the session I was deep thinking how can I do something quick and useful with kinnect! Of course every one else should have been doing the same, but as kinnect is designed for gaming, people tent to do some fun cool stuff with it. However, I am not a bug fan of computer games, so I thought I should do something different.
First thing came into my mind was of course using the depth camera for 3D object recognition and do a robot that moves around the house with a kinnect connected to the laptop. This could be an interesting idea, but sure it would be too much work and no real gain, except a bit of self satisfaction.
My second thought was to do something with the gesture recognition. Like having a cloud based gesture database and a bunch of algorithms to realize a gesture. Yes, this idea sounds promising for a bunch of reasons: First, it does not involve any screw driver (which I hate). Second, it is a database. Third, if it is done properly, people may use it. Third, I don’t actually need a kinnect for 98% of the work! (UPDATE: Check out this amazing free robot simulator by Microsoft and the channel 9 talk, where you can do all robotic things without having kinnect or using any screw or wire.)
Gesture recognition techniques are as old as computers, there has been enormous amount of research on it. This IEEE Transactions survey paper is a short survey about the most useful and handy techniques of gesture recognition. Although, there is a big body of knowledge there in the scientific community, I am not going to go that way. So don’t panic. First of all I am not interested to do a research degree on computer vision, and second I prefer a different look to the problem. A developer’s look.
Why not existing gesture recognition techniques:
The reason I think I would not need to adopt existing techniques is that, these scientists, did not have kinnect, so they could not look at the problem the way we can. Second, scientists deliberately make the problem artificially harder to get a better publication. We want to make the problem easier to get the app working.
Why not machine learning?
Don’t take me wrong. Machine learning is amazing, but not for a typical development project for a variety of reasons: First, machine learning is really hard to implement well, even though there are a few projects that can be utilized for this purpose (like google’s prediction API [http://code.google.com/apis/predict/]), it is still very hard to get something useful out of it. Second, how would you test you project that is depended to some machine learning? Third, how could you debug or fine tune your solution? You will be very limited to machine’s suggestion and believe me, machine’s suggestions can sometime really piss you off!
So lets plan a good approach for a gesture database. I would like to have a simple gesture database that works reasonably well at most of the times. Not planning to beat any existing techniques, but I need to work within the following constraints:
1- Gesture data should be recorded in a Sql Server.
2- Finding a gesture between tens of thousands of gestures should happen instantly. This infers some sort of index should exist.
3- A developer should be able to define a gesture purely using code (or strings). Necessary for testing your solution.
4- No crazy algorithm. Beauty is in simplicity.
5- You don’t need to have kinnect to work with the product.
I have already got a suggestion for solving this problem which is not tested but I think can work. Let’s build up a bit of theory around the story.
What is a gesture? Gesture is a bunch of movements of a bunch of body elements which infer a meaning.
What is a gesture but make it simpler? Gesture is pattern of movement of a bunch of point in a timely fashion starting from a starting point.
What is a geture? Please make in simple. Gesture starts with a pose. Then each point of the pose animate in a pattern.
What is a pose? If I am standing with my hands up, without moving, I am in a pose called Hands Up!.
Ok! Then how you make a gesture out of a pose? If I am in a pose (e.g. hands up) and start moving my hand in a left-right pattern for a while I am having a waving gesture.
Good! Now I understand gesture.
Let us assume a pose is location of a bunch of points in space. We define some basic terms for further use:
– Space: Let us assume space as the area that a man body fits with open hands. This is of course relative to the body but we normalize that area for every person into a cube.
– Pose: A collection of named points (like hand, head, elbow, knee, etc.) in space. The point may be defined with absolute positions or with relative positions from each other.
– Perspective: This is a key point for the rest of this approach. A pose can have multiple perspectives. Each perspective is mapping (or reflection) of the pose on one plate of the space. For example one perspective may show a flattened 2D image of a 3D body by correlating each point from the space to the plate. We need three 2D perspectives from a pose:
Top perspective (looking to the person from above).
Left perspective (looking to the person from left) and,
Front perspective (looking to the person from front).
For each 2D perspective, we generate two 1D perspectives:
Horizontal perspective (x position of points), and
Vertical perspective (y position of point)
Using above perspectives a shooting man pose can be expressed with a 6 1D perspectives. (2 1D perspective time three 2D perspectives).
Each 1D perspective can be described with a simple string:
Perspective: Left.vertical = “F.H1.H2”
Perspective: Left.horizontal = “H1.H2-F”
Let us use the following conventions:
F stands for “Face”
H1 stands for “right hand”
H2 stands for “left hand”
period(.) stands for very short distance
dash(-) stands for average distance
underline(_) stands for long distance
Hence, Left.vertical perspective of the shooting man says: There is a right hand very close to left hand then a bit further there is a head (if you look from the above).
Left.horizontal perspective of the shooting man says: There is left hand, right hand, and a face around the same point if you are looking from the front.
Bingo! We have a language to define a pose!
Let’s assume that the language is good and the string can be really used to define a pose. Of course it is not tested yet but I would be really thankful if someone experiments with a few real pictures.
Now that we can define poses, how can we find a similar pose from a database quickly?
Assuming the pose language is useful, this is not an issue at all. The problem is similar to fuzzy string matching problem. In fact a pose is a string, and you may want to find other string approximately similar to this one (for example, if the position of left and right hands of the shooting person is different it is still a shooting position).
It is not really a hard problem, trust me. It can be used using basic q-gram technique. The only difference is that we may need to have a specific equality comparison logic which can be easily implemented with a Clr type definition:
Here is the equality comparison logic:
H1.H2 is equal to H2.H1 (if two points are very close together we can use them interchangeably)
H2-H1 is not equal to H2-h1 (if the points are distant enough, they can’t be interchangeable)
User may define options for equality of objects, for example if the left hand and right hand does not matter to us, we can say Options.LeftAndRightHandsAreEqual!
I think it is enough for now. Using above technique we can have a pose database and a fast method to search a given pose within millions of poses and find other similar poses in a matter of milliseconds. We can do this for all the perspectives and shortlist the intersections for a much better search quality.
The next challenge (apart implementing and testing the technique) is to define the gesture after the pose.