Transitioning from Handcrafted to Deep Feature Extraction: Human Action Recognition

A transitional approach that emphasizes the development of feature extraction techniques is a strategy that aims to shift from the traditional handcrafted methods to the present deep learning methodologies. The different methodology includes extracting features using classical traditional methods with dense trajectories, temporal and spatial features like optical flow Magnitude, optical flow direction alongside advanced pre-trained deep learning models. The advent of deep learning has enabled the automatic extraction of significant features from raw data, compared to the traditional handcrafted feature extraction method and allowing for more complex actions to be recognized. This transition improves the performance in recognizing actions across diverse application areas including pedestrian navigation, video surveillance, sports analysis, remote patient monitoring, security, dance training, fall detection, human-computer interaction and locomotor rehabilitation. The transition from handcrafted feature extraction methods to deep learning approaches has significantly enhanced the accuracy and efficiency of human action recognition systems. The results highlight how these two approaches vary with each other: handcrafted features and deep learning features in terms of computational efficiency and accuracy.