Context Understanding in Real Time Video Analysis Based on Convolutional Neural Networks
Xiaoming Pan, Yinjun Zhang · 2024
This paper explores the application of Convolutional Neural Networks (CNNs) for context understanding in real-time video analysis. With the proliferation of video data across various domains, effective context interpretation is crucial for applications such as surveillance, autonomous driving, and interactive systems. We present a novel framework that leverages CNNs to extract spatial and temporal features from video streams, enabling enhanced recognition of complex scenes and interactions. Our experiments demonstrate significant improvements in accuracy and processing speed compared to traditional methods. We also discuss the implications of our findings for future research in video analysis and intelligent systems.