What Are the Best Practices for Building Low-Latency Vision AI Pipelines for Real-Time Video Analysis?

Real-time Vision AI systems require low-latency workflows to function effectively, as high-latency is unsuitable for applications needing immediate response, such as robotic control or live sports broadcasts. Key to achieving low latency is minimizing "glass-to-glass" time, the duration from when a photon hits a camera sensor to when the processed output is displayed. This involves optimizing each stage of the pipeline, from sensor exposure and encoding to network transmission and display lag. Choosing the right streaming protocol, such as SRT or WebRTC, is crucial as it sets the latency floor, with SRT offering reliability in unpredictable networks and WebRTC providing sub-500ms latency for browser-based applications. Model inference time can be reduced using techniques like INT8 quantization and Temporal Shift Modules, while deploying models at the edge rather than the cloud can eliminate network delays for immediate responses, with a hybrid approach offering both edge responsiveness and cloud accuracy. Furthermore, dedicated hardware accelerators like NVIDIA's Deep Learning Accelerators can offload tasks to improve throughput. By focusing on these aspects, systems can be optimized to process data quickly, ensuring that slightly noisy but timely data is prioritized over late, perfectly processed frames.