Process RTSP Streams for Real-Time Video Analytics

In 2025, wildfires in the United States burned over 5 million acres, highlighting the critical need for efficient wildfire detection systems, especially in California where improved response times could yield significant economic benefits. This guide details the development of a robust wildfire smoke detection pipeline using the Roboflow Inference Docker container, emphasizing challenges in processing RTSP streams which deliver continuous multimedia data. It highlights issues such as lag accumulation, stream drops, and threading when dealing with real-time video feeds from IP cameras. The pipeline structure is meticulously outlined, with components designed to handle RTSP ingestion, inference, and annotation, while utilizing a model from Roboflow Universe trained to detect fire and smoke. The guide further explains the setup of a local inference server via Docker, and the simulation of RTSP streams using MediaMTX and FFmpeg, ensuring the system is tested before deployment on real cameras. It also addresses buffering and stream reconnection, ensuring a resilient and responsive system that can be easily adapted to other detection tasks by modifying model parameters.