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Human action recognition (HAR) is always an enthralling topic because it facilitates the
identification of activity from the video's sequence. Applications for Human action
recognition is numerous including surveillance, sport analysis, suspicious activity
recognition and healthcare. Human activity recognition is hampered by poor resolution
cameras, extreme weather, and similar colors for both the subject and the object, as well
as by intraclass human activity such as walking and jogging. Currently available
approaches i.e. transformer based models, expanded datasets and improved temporal
modelling techniques such as attention mechanisms and LSTMs remove the background
noise from the final layers but the accuracy of correctly identifying actions is reduced and
address intraclass resemblance in human action classification to some extent. These
advancements improve the capabilities of action recognition systems but completely
resolving intraclass resemblance is a challenging task. Therefore, there is a growing need
for improved computer vision-based surveillance systems. A hybrid approach called
"Human Action Recognition using Deep Learning and Hybrid Evolutionary Techniques"
is proposed to address these issues. It consists of following main steps: preprocessing i.e.
contrast enhancement, data augmentation, customized models based on residual block
architecture, training Residual Block2 and Residual Block3 models, feature extraction
and testing, features fusion, feature selection using Binary Chimp optimization and
classification. To enhance interpretability, transparency and trust in machine learning
models, Grad-CAM and LIME are applied. Both these techniques provide visual display
of important regions in imaging. Grad-CAM gave heatmaps and LIME produced
highlighted regions on original images. Our suggested methodology achieves state-ofthe-art accuracy on the UT Interaction dataset of Action Recognition with 94% Accuracy.
This emphasizes how well the proposed technique works to improve the classification of
human actions.