Interactive Video Annotation Tool for Generating Ground Truth Information
Sung‐Joo Park, Chang Mo Yang · 2019
In this paper, we propose an interactive video and image annotation tool to generate ground truth information, that is essential information for training deep neural network. The proposed annotation tool not only generates various ground truth (GT) information such as object, motion, and event information, but also supports a semi-automatic video and image annotation method for fast generation of ground truth. The ground truth generated in the proposed tool is stored in the metadata database as a form of XML. The implementation results show that the proposed annotation tool provides faster and more detailed ground truth information compared to the existing methods.