A Learning-Based Data Collection Tool For Human Segmentation
Human segmentation is a difficult machine learning task of identifying and extracting the human in a picture. Most of the time this is done by using a convolutional neural network. In order to achieve an accurate and robust model, large amounts of data with varying human poses need to be collected to train the model. Collecting and labeling train data by hand takes lots of time and resources. This project explores another option to use automtation to collect and label pre-existing data from internet videos.
The model that was focused on is the DTEN ME model used for Zoom meetings virtual background.
