Leveraging FFT and Hybrid EfficientNet for Enhanced Action Recognition in Video Sequences

Human Action Recognition (HAR) has emerged as a pivotal challenge in the rapidly evolving realm of artificial intelligence and computer vision. This research delves into enhancing action recognition in video sequences by synergizing the Fast Fourier Transform (FFT) capabilities and the EfficientNet model. We employed the UCF101 dataset, encompassing a diverse range of action categories, to validate our approach empirically. The results underscored that incorporating FFT in the preprocessing stage amplifies the recognition efficacy of the EfficientNet model. Comparative analyses further substantiated the superiority of this combined approach over conventional methods, marking a significant contribution in the domain of action video recognition.