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What is Hybrid ML for Efficient Video Surveillance about?

This paper presents a novel approach to reduce computational complexity in video surveillance systems by integrating hybrid machine learning algorithms, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for event recognition. The proposed model enhances accuracy and efficiency while addressing challenges related to scalability and real-time processing through dynamic model scaling and edge computing. Simulation results indicate significant improvements in processing tim

Author

IAES IJAI

Language

EN