Training deep-learning-based vision systems requires the manual annotation of
a significant amount of data to optimize several parameters of the deep
convolutional neural networks. Such manual annotation is highly time-consuming
and labor-intensive. To reduce this burden, a previous study presented a fully
automated annotation approach that does not require any manual intervention.
The proposed method associates a visual marker with an object and captures it
in the same image. However, because the previous method relied on moving the
object within the capturing range using a fixed-point camera, the collected
image dataset was limited in terms of capturing viewpoints. To overcome this
limitation, this study presents a mobile application-based free-viewpoint
image-capturing method. With the proposed application, users can collect
multi-view image datasets automatically that are annotated with bounding boxes
by moving the camera. However, capturing images through human involvement is
laborious and monotonous. Therefore, we propose gamified application features
to track the progress of the collection status. Our experiments demonstrated
that using the gamified mobile application for bounding box annotation, with
visible collection progress status, can motivate users to collect multi-view
object image datasets with less mental workload and time pressure in an
enjoyable manner, leading to increased engagement.

提出了一种通过移动相机进行捕捉的自动化注释方法，并通过游戏化应用程序特性来捕获多视图图像数据集，降低了人们的心理负担和时间压力，从而增加了参与度。