How do you deploy offline AI models for real-time video analytics?
Real-time offline video analytics requires optimized pipelines: use YOLO v8/v9 (nano or small variants) for object detection at 30+ FPS on edge GPUs, implement tracking with ByteTrack or BoT-SORT for multi-object tracking without re-detection every frame, and use TensorRT or OpenVINO for hardware-specific optimization. Architecture pattern: decode frames with hardware acceleration (NVDEC/VA-API), run detection every Nth frame with tracking interpolation between detections, batch multiple camera streams for GPU efficiency. Memory management: use ring buffers for frame storage, implement ROI-based processing to analyze only relevant image regions, and apply model cascading (fast classifier first, detailed model only when triggered). For analytics aggregation, maintain in-memory counters with periodic disk persistence. Typical setup handles 8-16 camera streams at 720p on a single NVIDIA Jetson Orin.