Online Action Detection Incorporating an Additional Action Classifier
學年 113
學期 1
出版（發表）日期 2024-10-18
作品名稱 Online Action Detection Incorporating an Additional Action Classifier
作品名稱（其他語言）
著者 Hsu, M.-H., Hsu, C.-C., Wang, Y.-T., Huang, S.-K., Chien, Y.-H.
單位
出版者
著錄名稱、卷期、頁數 Electronics, vol.13, 4110
摘要 Most online action detection methods focus on solving a (K + 1) classification problem, where the additional category represents the ‘background’ class. However, training on the ‘background’ class and managing data imbalance are common challenges in online action detection. To address these issues, we propose a framework for online action detection by incorporating an additional pathway between the feature extractor and online action detection model. Specifically, we present one configuration that retains feature distinctions for fusion with the final decision from the Long Short-Term Transformer (LSTR), enhancing its performance in the (K + 1) classification. Experimental results show that the proposed method achieves an accuracy of 71.2% in mean Average Precision (mAP) on the Thumos14 dataset, outperforming the 69.5% achieved by the original LSTR method.
關鍵字 online action detection; LSTR; action classification
語言 en
ISSN
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Yin-Tien Wang
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版