IECE - Institute of Emerging and Computer Engineers

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    Academic Editor
    Contributions by role
    Editor 2
    ORCID
    0000-0002-8295-3862
    Guoxiong Zhou
    Central South University of Forestry and Technology, China
    Summary
    Edited Journals
    IECE Transactions on Emerging Topics in Artificial Intelligence
    IECE Contributions
    Articles
    0
    Edited
    2
    Code (Data) Available | Free Access | Research Article | Feature Paper | 09 August 2024
    LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition
    by
    Fafa Wang
    ,
    Xuebo Jin
    ,
    Shenglun Yi
    IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 58-70, 2024 | DOI:10.62762/TETAI.2024.628205
    Abstract
    This paper proposes an improved video action recognition method, primarily consisting of three key components. Firstly, in the data preprocessing stage, we developed multi-temporal scale video frame extraction and multi-spatial scale video cropping techniques to enhance content information and standardize input formats. Secondly, we propose a lightweight Inception-3D networks (LI3D) network structure for spatio-temporal feature extraction and design a soft-association feature aggregation module to improve the recognition accuracy of key actions in videos. Lastly, we employ a bidirectional LSTM network to contextualize the feature sequences extracted by LI3D, enhancing the representation capa... More >
    Graphical Abstract
    LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition
    Free Access | Research Article | Feature Paper | 29 May 2024
    CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes
    by
    Ming Gao
    ,
    Shixin Yang
    IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 44-57, 2024 | DOI:10.62762/TETAI.2024.240529
    Abstract
    In the era of rapid technological advancement, the demand for sophisticated Multi-Object Tracking (MOT) systems in applications such as intelligent surveillance and autonomous navigation has become increasingly critical.However, existing models often struggle with accuracy and efficiency in densely populated or dynamically complex environments. Addressing these challenges, we introduce a novel deep learning-based MOT model that incorporates the latest CT-DETR detection technology and an advanced ReID module for improved pedestrian tracking. Experimental results demonstrate the model's superior performance in accurately identifying and tracking multiple targets across varied scenarios, signif... More >
    Graphical Abstract
    CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes