Intelligent Video Surveillance System for Real Time Effective Human Action Recognition using Deep Learning Techniques
R. Sathya, M. Mythili, S. Ananthi, R. Asitha, V. Vardhini, Mariapun Shivaani · 2023
In computer vision, recognition of human action is an important and difficult task with many practical uses, inclusive of visual recording surveillance systems and human-computer interaction. The integration of KTH and real-time datasets within a framework for human action recognition in video surveillance using deep learning. The integration of real-world data with the well-established KTH dataset notably improves the action recognition models’ generalization and robustness. The real-time dataset adds richness to the training process by capturing a variety of dynamic human actions. The system uses Convolutional Neural Networks (CNNs) to categorize behaviors in real-time video streams by extracting spatiotemporal information from video frames. The results of the experiments indicate how well this integrated dataset approach performs, and how well it can recognize a wide range of activities under different settings. This method demonstrates an overall performance of 98.7% using a real-time dataset. This breakthrough has substantially increased the dependability and efficacy of video surveillance systems insecurity applications, guaranteeing a safer environment through the effective detection and tracking of human actions in real-time scenarios.