Application research of video image recognition technology based on CNN in real-time monitoring

The research focuses on the potential uses of Convolution Neural Networks (CNN) in Video Real-Time Analysis (VREA) to increase the effectiveness of image analysis in different fields including security systems, traffic control mechanisms, and industrial automation. Conventional approaches adopted for image recognition have the following constraints; they are computationally intensive, slow in terms of time response, and sensitive to environmental conditions such as occlusion, blur due to motion, and variation in illumination. Some techniques applied in the current framework include depth-wise separable convolutions, attention-based mechanisms, and temporal consistency modules to enable real-time analysis applied to videos. When CNN models are run on devices such as NVIDIA Jetson Nano, the edge Machine Learning (ML) framework increases latency performance, privacy, and resource optimization. Real-time operations like motion stabilization and eliminating blurring effects guarantee stable work in complex environments. Outcomes show that features extracted from different video frames can be detected using the proposed model with improvement in training and validation losses. As expected, several difficulties such as the class imbalance problem and high False Positive were evident in the results, suggesting the need for improvements in the features extraction and the thresholds used for classification. The work adds a relevant and efficient approach to video monitoring applications, especially through the proposed integration of CNN with edge computing. The research provides a good foundation on how CNN's real-time video monitoring system can be implemented to constrained resources.