Hi, everyone. My name is Daehwa Kim and I’ll introduce OddEyeCam,
which is a practical inside-out mobile device tracking system to support the body-centric peephole interaction.
This work was done by HCI Lab at KAIST.
Nowadays, mobile devices are becoming platforms for various applications.
However, their small screens make it difficult for users to use information-rich applications.
The space around the body not only expands the interaction space of a mobile device beyond its small screen
but also enables users to utilize their kinesthetic sense.
Therefore, body-centric peephole interaction has gained considerable attention.
Unfortunately, prior approaches considered a user’s face or initial position of the device as a reference frame,
which limits a reliable body-centered tracking when users are walking or their head and body are misaligned.
that human perceived trunk-centered space as whole-body-centered space.
An ideal sensing solution should track the user’s body as a reference frame and satisfy these four design goals.
First, it has to track the device position relative to the body in an absolute manner.
Second, it needs continuous tracking in a wide space around the body
to support various interaction scenarios.
Third, robust tracking is required without restricting the user’s behavior,
such as no need to keep the user’s head still.
Fourth, to completely support the mobile device’s portability,
it should provide a body-centric interface even when the user is walking.
In response, we propose OddEyeCam, which satisfies these design goals.
An OddEyeCam prototype consists of two cameras.
A narrow view depth camera misses a significant portion of a body and it makes body-tracking unreliable.
Therefore, OddEyeCam finds the body keypoints from a wide view RGB image
and then obtains corresponding depth information from a narrow view depth camera.
The pose estimation module finds body keypoints on a wide view image with reduced distortion.
As shown in two checkboard images below,
we applied perspective projection to the wide view image
to make it like a normal field of view image.
Using homography, the wide view image was cropped, transformed, and rotated to match the depth image.
Finally, both body keypoints and precise depth information around the user’s trunk can be obtained.
OddEyeCam builds a body coordinate system relative to the device
using shoulder keypoints and depth information.
Our method enables a precise 3D position tracking of a mobile device
with an average error of 4.3 cm.
Also, it needs no calibration step.
OddEyeCam can continuously track device position in a wide interaction space.
Also, OddEyeCam can robustly track device location even when users tilt the device.
With a combination of OddEyeCam and IMU inputs, our system can detect a user’s walking state
and provide appropriate information on a body-centered space when the user is walking.
With touch input, a user can naturally transfer files between apps.
We quantitatively evaluated our system through three experiments.
One user study showed that the peephole target selection task on OddEyeCam followed Fitt’s law,
and this suggests that OddEyeCam could support a natural human-hand movement.
Please see our paper for more technical details and evaluation results.
We opened our code on Github.
If you have any questions, please feel free to contact the authors through the below information.
Thanks for watching!