A Sliding Window Scheme for Online Temporal Action Localization
Young Hwi Kim, Hyolim Kang, Seon Joo Kim
Abstract
"Most online video understanding tasks aim to immediately process each streaming frame and output predictions frame-by-frame. For extension to instance-level predictions of existing online video tasks, Online Temporal Action Localization (On-TAL) has been recently proposed. However, simple On-TAL approaches of grouping per-frame predictions have limitations due to the lack of instance-level context. To this end, we propose Online Anchor Transformer (OAT) to extend the anchor-based action localization model to the online setting. We also introduce an online-applicable post-processing method that suppresses repetitive action proposals. Evaluations of On-TAL on THUMOS’14, MUSES, and BBDB show significant improvements in terms of mAP, and our model shows comparable performance to the state-of-the-art offline TAL methods with a minor change of the post-processing method. In addition to mAP evaluation, we additionally present a new online-oriented metric of early detection for On-TAL, and measure the responsiveness of each On-TAL approach."
BibTeX
@inproceedings{eccv2022_aslidingwindowsc,
title = {A Sliding Window Scheme for Online Temporal Action Localization},
author = {Young Hwi Kim and Hyolim Kang and Seon Joo Kim},
booktitle = {ECCV 2022},
year = {2022}
}