An efficient human action recognition framework based on hybrid features and enhanced long short term memory Online publication date: Thu, 24-Aug-2023
by B. Suresh Kumar; S. Viswanadha Raju
International Journal of System of Systems Engineering (IJSSE), Vol. 13, No. 3, 2023
Abstract: Although the scientific group has given considerable attention, an effective method of detecting human activity in the physical realm remains elusive because of variations in appearance, interactions between objects, and mutual occlusion. So, in this study, an efficient human action recognition (HAR) system based on hybrid features with enhanced long short-term memory (ELSTM) is proposed. Initial key frames are extracted from a sequence of input videos using the structural similarity measure (SSIM). After that, the features namely, coverage factor, space-time interest (STI) points, and shape features are extracted from the key frames. Then, the selected features are fed to the ELSTM classifier to classify a diffident activity of a human. The proposed ELSTM is designed by LSTM with the integration of adaptive golden eagle optimisation (AGEO) in the weight update process to select possible weights. The proposed approach is evaluated in terms of' accuracy, precision, recall, and F-Measure.
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