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    CAMNet: DeepGait Feature Extraction via Maximum Activated Channel Localization

    Salisu Muhammed*, Erbuğ Çelebi

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 397-416, 2021, DOI:10.32604/iasc.2021.016574

    Abstract As the models with fewer operations help realize the performance of intelligent computing systems, we propose a novel deep network for DeepGait feature extraction with less operation for video sensor-based gait representation without dimension decomposition. The DeepGait has been known to have outperformed the hand-crafted representations, such as the frequency-domain feature (FDF), gait energy image (GEI), and gait flow image (GFI), etc. More explicitly, the channel-activated mapping network (CAMNet) is composed of three progressive triplets of convolution, batch normalization, max-pooling layers, and an external max pooling to capture the Spatio-temporal information of multiple frames in More >

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