Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162581
Title: Effective action recognition with embedded key point shifts
Authors: Cao, Haozhi
Xu, Yuecong
Yang, Jianfei
Mao, Kezhi
Yin, Jianxiong
See, Simon
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Cao, H., Xu, Y., Yang, J., Mao, K., Yin, J. & See, S. (2021). Effective action recognition with embedded key point shifts. Pattern Recognition, 120, 108172-. https://dx.doi.org/10.1016/j.patcog.2021.108172
Journal: Pattern Recognition
Abstract: Temporal feature extraction is an essential technique in video-based action recognition. Key points have been utilized in skeleton-based action recognition methods but they require costly key point annotation. In this paper, we propose a novel temporal feature extraction module, named Key Point Shifts Embedding Module (KPSEM), to adaptively extract channel-wise key point shifts across video frames without key point annotation. Key points are adaptively extracted as feature points with maximum feature values at split regions and key point shifts are the spatial displacements of corresponding key points. The key point shifts are encoded as the overall temporal features via linear embedding layers in a multi-set manner. Our method achieves competitive performance through embedding key point shifts with trivial computational cost, achieving the state-of-the-art performance of 78.81% on Mini-Kinetics and competitive performance on UCF101, Something-Something-v1 and HMDB51 datasets.
URI: https://hdl.handle.net/10356/162581
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2021.108172
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Page view(s)

9
Updated on Nov 26, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.