Prof. lamiaa Elrefaei :: Publications:

Human action recognition has been identified as an important research topic in computer vision because it is an essential form of communication and interplay between computers and humans to assist computers in automatically recognizing human behaviors and accurately comprehending human intentions. Inspired by some keyframe extraction and multifeatured fusion research, this paper improved the accuracy of action recognition by utilizing keyframe features and fusing them with video features. In this article, we suggest a novel multi-stream approach architecture made up of two distinct models fused using different fusion techniques. The first model combines convolutional neural networks in two-dimensional (2D-CNN) with long-short term memory networks to glean long-term spatial and temporal features from video keyframe images for human action recognition. The second model is a three-dimensional convolutional neural network (3D-CNN) that gathers quick spatial–temporal features from video clips. Subsequently, two frameworks are put forth to explain how various fusion structures can improve the performance of action recognition. We investigate methods for video action recognition using early and late fusion. While the late-fusion framework addresses the decision fusion from the two models' choices for action recognition, the early-fusion framework examines the impact of early feature fusion of the two models for action recognition. The various fusion techniques investigate how much each spatial and temporal feature influences the recognition model's accuracy. The HMDB-51 and UCF-101 datasets are two important action recognition benchmarks used to evaluate our method. When applied to the HMDB-51 dataset and the UCF-101 dataset, the early-fusion strategy achieves an accuracy of 70.1 and 95.5%, respectively, while the late-fusion strategy achieves an accuracy of 77.7 and 97.5%, respectively.