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.

翻译：基于深度学习的视觉系统训练需要手动标注大量数据，以优化深度卷积神经网络的多个参数。这种手动标注过程耗时且劳动强度大。为减轻这一负担，先前研究提出了一种无需任何人工干预的全自动标注方法。该方法通过将视觉标记与物体关联，并在同一图像中捕获两者。然而，由于先前方法依赖固定摄像头在拍摄范围内移动物体，所采集的图像数据集在视角方面存在局限性。为突破这一限制，本研究提出了一种基于移动应用的自由视角图像采集方法。通过该应用，用户可通过移动摄像头自动获取多视角图像数据集，并完成边界框标注。但人工拍摄过程仍显繁琐单调，因此我们设计了游戏化应用功能以追踪采集进度。实验证明，使用具备可见采集进度状态的游戏化移动应用进行边界框标注，能有效激励用户在更少心理负荷和时间压力下愉快地采集多视角物体图像数据集，从而提升参与度。