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JACIII Vol.25 No.4 pp. 404-409
doi: 10.20965/jaciii.2021.p0404
(2021)

Paper:

Anno-Mate: Human–Machine Collaboration Features for Fast Annotation

John Anthony C. Jose, Meygen D. Cruz, Jefferson James U. Keh, Maverick Rivera, Edwin Sybingco, and Elmer P. Dadios

De La Salle University
2401 Taft Avenue, Manila 1004, Philippines

Corresponding author

Received:
February 10, 2021
Accepted:
April 15, 2021
Published:
July 20, 2021
Keywords:
video annotations, object detection, object tracking, annotations, human–machine collaboration
Abstract

Large annotated datasets are crucial for training deep machine learning models, but they are expensive and time-consuming to create. There are already numerous public datasets, but a vast amount of unlabeled data, especially video data, can still be annotated and leveraged to further improve the performance and accuracy of machine learning models. Therefore, it is essential to reduce the time and effort required to annotate a dataset to prevent bottlenecks in the development of this field. In this study, we propose Anno-Mate, a pair of features integrated into the Computer Vision Annotation Tool (CVAT). It facilitates human–machine collaboration and reduces the required human effort. Anno-Mate comprises Auto-Fit, which uses an EfficientDet-D0 backbone to tighten an existing bounding box around an object, and AutoTrack, which uses a channel and spatial reliability tracking (CSRT) tracker to draw a bounding box on the target object as it moves through the video frames. Both features exhibit a good speed and accuracy trade-off. Auto-Fit garnered an overall accuracy of 87% and an average processing time of 0.47 s, whereas the AutoTrack feature exhibited an overall accuracy of 74.29% and could process 18.54 frames per second. When combined, these features are proven to reduce the time required to annotate a minute of video by 26.56%.

Cite this article as:
John Anthony C. Jose, Meygen D. Cruz, Jefferson James U. Keh, Maverick Rivera, Edwin Sybingco, and Elmer P. Dadios, “Anno-Mate: Human–Machine Collaboration Features for Fast Annotation,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.4, pp. 404-409, 2021.
Data files:
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Last updated on Nov. 25, 2021