Google has turned to its video-sharing site YouTube to develop a labelled dataset for human actions. The company is using the data from more than 1 million videos uploaded by users in order to train machine-learning algorithms that can recognise and label actions such as “like” or “share”. AVA could benefit companies such as Lighthouse, which develops security systems, and video detection firm Matroid.
Google has turned to its video-sharing site YouTube to develop a labelled dataset for human actions. Dubbed atomic visual actions, or AVA for short, the dataset is made up of more than 57,000 three-second video segments and comprises more than 210,000 total labels, enabling computer vision programmes to better understand complex scenes. AVA could benefit companies such as Lighthouse, which develops security systems, and video detection firm Matroid.