Introducing AI into existing camera systems not only improves monitoring efficiency and accuracy but also enables intelligent scene analysis and early warning capabilities. By selecting appropriate deep learning models, optimizing real-time video inference technology, adopting a hybrid edge computing and cloud architecture, and implementing containerized and scalable deployment, AI technology can be effectively integrated into existing camera systems.
Introducing AI Technologies
Deep Learning Model Selection and Optimization
Deep learning models are the “brains” of video surveillance systems, responsible for extracting and analyzing information from video frames. Selecting the right deep learning model is crucial for improving system performance. Common deep learning models include:
YOLO series: Suitable for scenarios with high real-time requirements, such as traffic monitoring.
Faster R-CNN: Suitable for scenarios with high accuracy requirements, such as industrial defect detection.
Visual Transformer (ViT): Excels at processing complex scenes and long time series data.
To improve model training efficiency and performance, the following optimization techniques can be used:
Transfer learning: Leveraging pretrained models to reduce training time and data requirements.
Data sharding: Improves computing efficiency.
Real-time video inference technology: Real-time video inference is a key function in surveillance systems, and its efficiency depends on hardware and optimization techniques. Common technical approaches include: TensorRT: Accelerates model inference. Asynchronous inference architecture: Processes multiple video streams without blocking tasks. In terms of hardware support, GPUs and FPGAs excel in high-concurrency scenarios, while NPUs in edge devices balance performance and energy efficiency.
A hybrid architecture combining edge computing and the cloud enables smarter deployment models. Edge computing offers the advantage of real-time performance, eliminating the need for network transmission. Cloud-based analytics can store historical data and conduct large-scale pattern analysis. For example, a security system performs routine personnel flow analysis on edge devices, while offloading complex criminal behavior pattern analysis to cloud servers.
Containerization and Scalable Deployment
Containerization technologies (such as Docker and Kubernetes) enable rapid system deployment and easy updates and expansion. Through containerization, developers can package AI models and related dependencies together, ensuring stable operation in various environments.
Application Cases of Introducing Artificial Intelligence
AI Video Surveillance in Smart Cities
In smart cities, AI technology is widely used in video surveillance systems to improve urban management efficiency and safety. For example, cameras mounted on smart poles use biometric and pattern recognition technologies to automatically detect vehicles and pedestrians violating traffic rules and alert them. This application not only improves traffic management efficiency but also reduces the need for human intervention.
Intelligent Traffic Management
In the field of intelligent transportation, AI technology is being used to optimize traffic signal control, predict traffic flow, and automatically detect traffic accidents. For example, Metropolis City has integrated adaptive signal control technology at intersections. This technology, combined with AI algorithms, uses inductive loop sensors and video detection systems to capture real-time data and dynamically optimizes traffic signal durations using machine learning models. This technology has significantly reduced vehicle delays and improved traffic service quality.
Introducing AI into existing camera systems not only improves monitoring efficiency and accuracy but also enables intelligent scene analysis and early warning capabilities. By selecting appropriate deep learning models, optimizing real-time video inference technology, adopting a hybrid edge computing and cloud architecture, and implementing containerized and scalable deployment, AI technology can be effectively integrated into existing camera systems.
Post time: Jul-31-2025