Temporal Filtering Networks for Online Action Detection

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 57
  • Download : 0
Online action detection aims to detect a current action from an untrimmed, streaming video, where only current and past frames are available. Recent methods for online action detection have focused on how to model discriminative representations from temporally partial information. However, they overlook the fact that the input video contains background as well as actions. To overcome this problem, in this paper, we propose a novel approach, named Temporal Filtering Network, to distinguish between relevant and irrelevant information from a partially observed, untrimmed video. Specifically, we present a filtering module to learn relevance scores indicating how relevant the information is to a current action. Our filtering module emphasizes the relevant information to a current action, while it filters out the information of background and unrelated actions. We conduct extensive experiments on THUMOS-14 and TVSeries datasets. On these datasets, the proposed method outperforms state-of-the-art methods by a large margin. We also show the effectiveness of the filtering module through comprehensive ablation studies.
Publisher
ELSEVIER SCI LTD
Issue Date
2021-03
Language
English
Article Type
Article
Citation

PATTERN RECOGNITION, v.111, pp.107695

ISSN
0031-3203
DOI
10.1016/j.patcog.2020.107695
URI
http://hdl.handle.net/10203/279997
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0