Real Time Video Surveillance Using Pyramidal CNN
Manu Sharma, Shivam Kumar Singh, Swasti Singhal · 2024
In this research paper, we present a detailed view of the two convolutional neural network (CNN) architectures, namely, traditional CNN and Pyramidal CNN, for their effectiveness in video surveillance applications. The goal of this paper is to evaluate and analyse the performance of these two deep learning architectures in terms of speed and accuracy when applied to video surveillance tasks. Our results indicate that the Pyramidal CNN architecture outperforms the traditional CNN model regarding speed and accuracy. The Pyramidal CNN exhibits superior object detection and recognition capabilities, achieving faster processing times while maintaining high precision levels. This finding holds significant implications for real-time. Video surveillance applications, where rapid and accurate video data analysis is essential.