@Article{iasc.2021.016574,
AUTHOR = {Salisu Muhammed, Erbuğ Çelebi},
TITLE = {CAMNet: DeepGait Feature Extraction via Maximum Activated Channel Localization},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {28},
YEAR = {2021},
NUMBER = {2},
PAGES = {397--416},
URL = {http://www.techscience.com/iasc/v28n2/42064},
ISSN = {2326-005X},
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 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.},
DOI = {10.32604/iasc.2021.016574}
}