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Abstract
This article explores the application of neural networks, specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), in real-time object detection and classification within video streams. With the rapid evolution of computer vision technologies, neural networks have become the cornerstone of modern object detection systems. The paper delves into the architecture of these networks, their integration into real-time systems, and the accuracy and efficiency they provide for various applications such as autonomous vehicles, surveillance, and human-computer interaction.
References
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