Spatio-temporal Localization of Actors in Video/360-Video and its Applications

The popularity of platforms for storing and transmitting video content has created a substantial volume of video data. Given a set of actors present in a video, generating metadata with the temporal determination of the interval in which each actor is present and their spatial 2D localization in each frame in these intervals can facilitate video retrieval and recommendation. In this work, we investigate Video Face Clustering for this spatio-temporal localization of actors in videos. We first describe our method for Video Face Clustering in which we take advantage of face detection, embeddings, and clustering methods to group similar faces of actors in different frames and provide the spatio-temporal localization of them. Then, we explore, propose, and investigate innovative applications of this spatio-temporal localization in three different tasks: (i) Video Face Recognition, (ii) Educational Video Recommendation and (iii) Subtitles Positioning in 360-video.