We address temporal localization of events in large-scale video data, in the context of the Youtube-8M Segments dataset. This emerging field within video recognition can enable applications to identify the precise time a specified event occurs in a video, which has broad implications for video search. To address this we present two separate approaches: (1) a gradient boosted decision tree model on a crafted dataset and (2) a combination of deep learning models based on frame-level data, video-level data, and a localization model. The combinations of these two approaches achieved 5th place in the 3rd Youtube-8M video recognition challenge.
@article{arxiv.1910.11631,
title = {Learning to Localize Temporal Events in Large-scale Video Data},
author = {Mikel Bober-Irizar and Miha Skalic and David Austin},
journal= {arXiv preprint arXiv:1910.11631},
year = {2019}
}