InceptionV3-Based Human Action Detection: An Extensive Analysis of Deep Learning Methods for Enhanced Classification Performance

This work uses the deep and complex architecture of the InceptionV3 model to evaluate its effectiveness for human action detection, hence improving performance in action identification tasks. Renowned for both efficiency and depth, the InceptionV3 model comprises about 315 layers. This comprises of 159 convolutional layers extracting hierarchical features from input images, 122 batch normalizing layers stabilizing and accelerating training, and 122 activation layers adding non-linearity into the model. In order to lower dimensionality, the architecture also includes 21 concatenate layers to combine feature maps, and one global average pooling layer to compile feature representations. Usually featuring 1-2 dense layers, the model ends with a final decision boundary for categorization. This work shows the robustness and capacity of the InceptionV3 model in identifying challenging human actions by attaining an accuracy of 92% in human action detection tasks. Deep convolutional and normalizing layers of the model’s advanced design help to explain its capacity to learn and generalize from many activity patterns. The great accuracy emphasizes how well InceptionV3 extracts relevant characteristics and distinguishes between various human activities, therefore providing a useful tool for real-time action detection systems. This work emphasizes the possibility of using advanced deep learning architectures like InceptionV3 to solve problems in human action recognition and raise system performance.