IECE - Institute of Emerging and Computer Engineers

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    Academic Editor
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    Editor 1
    ORCID
    0000-0002-9247-9009
    Aniruddha Chandra
    National Institute of Technology, Durgapur, India
    Summary
    Aniruddha Chandra received BE, ME, and PhD degrees from Jadavpur University, Kolkata, India, in 2003, 2005, and 2011 respectively. He joined the Electronics and Communication Engineering Department, National Institute of Technology, Durgapur, India, in 2005. He is currently serving as an Associate Professor there. In 2011, he was a Visiting Lecturer at the Asian Institute of Technology, Bangkok. From 2014 to 2016, he worked as a Marie Curie fellow at Brno University of Technology, Czech Republic. Dr. Chandra has published more than 100 research papers in refereed journals and peer-reviewed conferences. He is a co-recipient of the best short paper award at IEEE VNC 2014, held in Paderborn, Germany, and delivered a keynote lecture at IEEE MNCApps 2012, held in Bangalore, India. He is currently the secretary of the IEEE P2982 Standard working group and IEEE ComSoc RCC SIG on Propagation Channels for 5G and Beyond. His primary area of research is physical layer issues in wireless communication.
    Edited Journals
    IECE Transactions on Sensing, Communication, and Control
    IECE Contributions
    Articles
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    Edited
    1
    Research Article | 25 October 2024
    Spatio-temporal Feature Soft Correlation Concatenation Aggregation Structure for Video Action Recognition Networks
    by
    Fafa Wang
    ,
    Shenglun Yi
    IECE Transactions on Sensing, Communication, and Control | Volume 1, Issue 1: 60-71, 2024 | DOI:10.62762/TSCC.2024.212751
    Abstract
    The efficient extraction and fusion of video features to accurately identify complex and similar actions has consistently remained a significant research endeavor in the field of video action recognition. While adept at feature extraction, prevailing methodologies for video action recognition frequently exhibit suboptimal performance in the context of complex scenes and similar actions. This shortcoming arises primarily from their reliance on uni-dimensional feature extraction, thereby overlooking the interrelations among features and the significance of multi-dimensional fusion. To address this issue, this paper introduces an innovative framework predicated upon a soft correlation strategy... More >
    Graphical Abstract
    Spatio-temporal Feature Soft Correlation Concatenation Aggregation Structure for Video Action Recognition Networks