Hi, my name is Hyunggoog Seo and I am PhD student at Vsual Media Lab, KAIST
Today I'd like to share with you our work,
Over there which is a simple and intuitive object registration method for an absolute mid-air pointing interface.
With the development of technology, various kinds of new home devices have emerged.
The increasing number of devices forces the user to manage diverse remote controls,
causing much inconvenience.
An absolute mid-air pointing technique can be a promising alternative,
to interact with the devices in a smart home environment.
Among the mid-air pointing techniques, a freehand pointing interaction
would be the most intuitive and natural way to select a target.
However, it is difficult to construct an environment where freehand pointing is available.
To enable freehand pointing interaction, we need a camera that observes the user.
And we also need a cumbersome preprocess called object registration.
Then, what is the object registration?
The absolute mid-air pointing interface identifies the object
pointed to by the user via raycasting.
However, at first, the system does not know what and where devices are in the camera coordinate system.
Objective registration makes the system know
what and where each device is in the camera coordinate system.
In previous work, object registration was achieved by using conventional calibration techniques or
high-tech hardware.
However, these approaches are too complicated,
or expensive which are not acceptable at an end-user level.
To tackle this problem, we introduce Overthere which is a simple and intuitive object registration method.
The key idea of Overthere is a human-in-the-loop strategy.
To identify object position, Overthere captures the multiple pointing gestures
and calculate the intersection of those gestures.
We regard the intersection point as the target position.
By adopting this strategy, Overthere allows ordinary people to register a target object simply.
Here is our user scenario.
The registration starts with object type selection.
In this step, the user simply selects the type of the device to be registered.
Then, the user points to a target at multiple viewpoints
and the system captures the pointing gestures.
A pointing gesture aimed at a target can be represented as a 3D straight line.
The origin of the ray is the head position,
and the direction vector is the head-hand vector of the user.
When there are multiple pointing gestures, they do not intersect each other because they are skew lines.
So we approximate the nearest point from all straight lines by using a least square method.
Overthere regards this point as a target position.
After each pointing gesture, the user can confirm the registration result
based on three types of feedback.
The user can decide to accept or discard current registration by intuitively evaluating the current estimation result
based on this feedback.
As a result, the feedback ensures the user to achieve reliable registration results.
Here is an example scenario in a smart home environment.
The user first registers the devices using Overthere.
After the registration is complete, the user can select target devices with a pointing gesture.
By utilizing other natural user interfaces, such as hand gestures,
the user can control the devices with freehand interaction.
We conducted three experiments to investigate the characteristics of our system.
The goal of the first study is to identify the optimal pointing motion
that leads to accurate and coherent pointing results.
Because our method utilizes human pointing gestures.
the pointing gesture itself should be accurate and coherent for the accurate registration result.
We compare the pointing results formed by three pointing motions.
The experimental results showed that the grab motion is most accurate and coherent in a Kinect environment.
The purpose of the second study is to identify the factors that can affect the accuracy of the estimated position.
The experimental results showed that the maximum angle between pointing gestures
and size of the target affect the estimation error.
Study 3 was carried out to investigate the
performance of Overthere and effect of feedback on error and time.
The average estimation error was about 19 cm,
and completion time for each device was about 28 seconds.
The feedback improves the accuracy of estimation, but it makes the completion time delayed.
This is it for the presentation thank you