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CAMNet: DeepGait Feature Extraction via Maximum Activated Channel Localization

Salisu Muhammed*, Erbuğ Çelebi

Computer Engineering Department, Cyprus International University, Nicosia, North Cyprus, Mersin 10, 099010, Turkey

* Corresponding Author: Salisu Muhammed. Email:

Intelligent Automation & Soft Computing 2021, 28(2), 397-416.


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 one gait period. We conducted experiments to validate the effectiveness of the proposed novel algorithm in terms of cross-view gait recognition in both cooperative and uncooperative settings using the state-of-the-art OU-ISIR multi-view large population OU-MVLP dataset. The OU-MVLP dataset includes 10307 subjects. As a result, we confirmed that the proposed method significantly outperformed state-of-the-art approaches using the same dataset at the rear angles of 180, 195, 210, and 225, in both cooperative and uncooperative settings for verification scenarios.


Cite This Article

S. Muhammed and E. Çelebi, "Camnet: deepgait feature extraction via maximum activated channel localization," Intelligent Automation & Soft Computing, vol. 28, no.2, pp. 397–416, 2021.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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