Computer vision is applied in an ever expanding range of applications, many of which require custom training data to perform well. We present a novel interface for rapid collection and labeling of training images to improve computer vision based object detectors. LabelAR leverages the spatial tracking capabilities of an AR-enabled camera, allowing users to place persistent bounding volumes that stay centered on real-world objects. The interface then guides the user to move the camera to cover a wide variety of viewpoints. We eliminate the need for post-hoc manual labeling of images by automatically projecting 2D bounding boxes around objects in the images as they are captured from AR-marked viewpoints. In a user study with 12 participants, LabelAR significantly outperforms existing approaches in terms of the trade-off between model performance and collection time.
Title
LabelAR: A spatial guidance interface for fast computer vision image collection
Published
2019-05-17
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2019-58
Type
Text
Extent
35 p
Archive
The Engineering Library
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