Adaptive frame selection in two dimensional convolutional neural network action recognition

We presented a technique in this research for dynamic frame selection to achieve robust features. This situation results in less redundancy and useful input for the network. Because it uses fewer processing resources and offers adequate accuracy, the suggested technique is appropriate for real-time applications. The network becomes more efficient and maintains adequate accuracy when informative frames are chosen and computation is minimized. The framework is tested on UCFIOI as one of the large and realistic datasets. The experiments show acceptable results employing both Resnet-50 and Mobilenet pre-trained features.