Maximum RTSP Camera Feeds for Orin Nano 8GB Face Detection

Issue Overview

Users are inquiring about the maximum number of 4MP RTSP camera feeds that can be simultaneously processed on an NVIDIA Jetson Orin Nano 8GB board. The specific use case involves running a basic face detection model using DeepStream while analyzing and displaying the feeds in real-time. The main concern is determining the board’s capacity to handle multiple high-resolution RTSP streams concurrently without compromising real-time performance.

Possible Causes

1. Hardware limitations: The Jetson Orin Nano 8GB board may have constraints in terms of processing power, memory, or bandwidth that limit the number of simultaneous RTSP feeds it can handle.

2. Software optimization: Inefficient implementation of the face detection model or DeepStream pipeline could reduce the number of streams that can be processed in real-time.

3. Network bandwidth: The network infrastructure may not support the required bandwidth for multiple 4MP RTSP streams, causing bottlenecks.

4. Decoder capabilities: The hardware decoder on the Orin Nano may have limitations on the number of concurrent H264/H265 streams it can process.

5. Thermal constraints: Processing multiple high-resolution streams may cause thermal issues, potentially limiting performance.

Troubleshooting Steps, Solutions & Fixes

1. Assess hardware decoder capabilities:

   • Refer to section 2.9.1 in the NVIDIA Developer documentation for specific information on the Orin Nano’s hardware decoder capabilities.
   • This will provide insight into the maximum number of H264/H265 streams the board can handle.

2. Optimize DeepStream pipeline:

   • Ensure your DeepStream application is properly optimized for the Orin Nano.
   • Use hardware-accelerated plugins where possible to maximize performance.
   • Consider batching multiple streams for more efficient processing.

3. Monitor system resources:

   • Use tools like top, nvidia-smi, and tegrastats to monitor CPU, GPU, and memory usage while running your application.
   • Identify potential bottlenecks and adjust your pipeline accordingly.

4. Experiment with different resolutions:

   • If 4MP proves too demanding, try reducing the resolution of the RTSP streams.
   • Find a balance between resolution and the number of concurrent streams.

5. Implement stream prioritization:

   • Consider a dynamic approach where you process a subset of streams at full resolution and others at lower resolution or framerate.

6. Optimize face detection model:

   • Use a lighter, more efficient face detection model if possible.
   • Consider using TensorRT for model optimization on the Jetson platform.

7. Test with incremental loads:

   • Start with a single RTSP stream and gradually increase the number.
   • Monitor performance and quality to determine the optimal number of streams.

8. Check network infrastructure:

   • Ensure your network can handle the bandwidth required for multiple 4MP RTSP streams.
   • Use a dedicated network for camera feeds if possible to minimize interference.

9. Thermal management:

   • Monitor the board’s temperature during operation.
   • Ensure proper cooling and ventilation to maintain optimal performance.

10. Update software:

    • Ensure you’re using the latest JetPack and DeepStream versions, as they may include performance improvements.

11. Consider distributed processing:

    • If a single Orin Nano can’t handle the required number of streams, consider distributing the workload across multiple boards.

Remember that the exact number of RTSP streams you can process simultaneously will depend on various factors, including the complexity of your face detection model, the efficiency of your DeepStream pipeline, and the specific characteristics of your RTSP streams. It’s crucial to test thoroughly with your actual setup to determine the optimal configuration for your use case.