Human interaction recognition in YouTube videos

This paper introduces the use of annotation tags for human activity recognition in video. Recent methods in human activity recognition use more complex and realistic datasets obtained from TV shows or movies, which makes it difficult to obtain the high recognition accuracies. We improve the recognition accuracies using annotation tags of the video. Tags tend to be related to video contents, and human activity videos frequently contain tags relevant to their activities. We first collect a human activity dataset containing tags from YouTube. Under this dataset, we automatically discover relevant tags and their correlation with human activities. We finally develop a framework using visual content and tags for activity recognition. We show that our approach can improve recognition accuracies compared with other approaches that only use visual content.