A Framework for Human Activity Recognition in Multiview Environment Based on URILBP and ConvSTLSTM

Human Activity Recognition(HAR) is an essential field of research with numerous applications in human-computer interaction, security, surveillance, and healthcare. Even with significant improvements, recognizing activities in the real world, multiview scenarios remain a challenging task due to factors like scale variations, cluttered background, and variations in viewpoints. Therefore, in order to deal with these issues, this paper presents a robust and view-invariant framework for human activity recognition. This paper presents a framework to efficiently utilize both handcrafted and deep features for temporal modeling in a multiview human activity recognition. In order to make the feature as being scale and rotation in-variant we applied the Uniform Rotation-Invariant Local Binary Patterns (URI-LBP) which is a variant of local binary pattern. Subsequently, a streamelined VGG16 model with 10 layers is utilized to extract high-level spatial features while maintaining computational efficiency. These complementary features are then fed into a Spatio-Temporal Long Short-Term Memory (ST-LSTM) network, which captures temporal dependencies across video frames, enabling accurate recognition of dynamic activities. Comprehensive analyses on the benchmark datasets CASIA and IXMAS illustrate the efficiency of the proposed framework, with remarkable accuracies of 95.68% and 98.78%, respectively. The outcomes demonstrate the suggested methodology's resilience and versatility in difficult multiview human activity recognition situations.