• Rashmika K. Vaghela

Real-time human action recognition in videos is a challenging task with numerous applications in fields such as surveillance, sports analysis, and human-computer interaction. This paper presents a novel approach for real-time human action recognition using spatiotemporal features and convolutional neural networks (CNNs). The proposed method leverages both spatial and temporal information in video sequences to capture the dynamic nature of human actions. Specifically, spatiotemporal features are extracted from video frames using techniques such as optical flow and motion history images. These features are then fed into a CNN architecture designed to learn discriminative representations of human actions. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed approach in achieving high accuracy and real-time performance in human action recognition tasks.

Convolutional neural networks (CNNs), Real-time human action recognition, Spatiotemporal features, Video analysis, Deep learning.

" REAL – TIME HUMAN ACTION RECOGNITION IN VIDEOS USING SPATIALTEMPORAL FEATURES AND CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 5, page no.5-15, May-2020, Available :http://www.jetir.org/papers/JETIR2005491.pdf

" REAL – TIME HUMAN ACTION RECOGNITION IN VIDEOS USING SPATIALTEMPORAL FEATURES AND CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 5, page no. pp5-15, May-2020, Available at : http://www.jetir.org/papers/JETIR2005491.pdf