Increasing Latency When Using Gstreamer in OpenCV

Issue Overview

Users are experiencing increased latency when using Gstreamer with OpenCV on the Nvidia Jetson Orin Nano Dev board. The issue manifests during the streaming of video data from two cameras: one connected to a Raspberry Pi and the other directly to the Jetson board. While using Gstreamer commands directly in the terminal results in approximately 500ms latency, incorporating these commands into OpenCV leads to a significant increase in latency that accumulates over time, causing delays in object detection tasks. The problem persists even when attempting to stream from only one camera.

Symptoms:

• Increased latency when streaming video through OpenCV.
• Unresponsiveness of the Python script after initial frame capture.
• Crashes occurring after a brief period of streaming.

Context:

• The issue occurs during the integration of Gstreamer pipelines into OpenCV applications.
• The user is running OpenCV 4.4.0 on a Linux environment with specific configurations detailed in the forum discussion.

Hardware/Software Specifications:

• Operating System: Linux 5.10.104-g16215e6bc aarch64
• OpenCV Version: 4.4.0
• Gstreamer Version: 1.16.3
• Commands Used:
  • Raspberry Pi: raspivid ... | gst-launch-1.0 ...
  • Jetson: gst-launch-1.0 ...

Impact:

The increasing latency severely affects the user experience, particularly for real-time applications like object detection, rendering the system less effective for its intended purpose.

Possible Causes

1. Hardware Incompatibilities or Defects:

   • Certain camera models may not be fully compatible with the Jetson Orin Nano, leading to performance issues.

2. Software Bugs or Conflicts:

   • Bugs within OpenCV or Gstreamer could lead to improper handling of video streams, causing latency.

3. Configuration Errors:

   • Incorrect parameters in Gstreamer commands (e.g., missing flags) may exacerbate latency issues.

4. Driver Issues:

   • Outdated or incompatible drivers for Gstreamer or OpenCV may hinder performance.

5. Environmental Factors:

   • Network conditions (e.g., TCP vs UDP) can affect streaming performance and introduce additional latency.

6. User Errors or Misconfigurations:

   • Misconfigured Gstreamer pipelines or incorrect threading models in Python could lead to unresponsiveness and crashes.

Troubleshooting Steps, Solutions & Fixes

1. Diagnosing the Problem:

   • Verify current configurations and command syntax used in both Gstreamer and OpenCV.
   • Check system logs for any error messages related to Gstreamer or OpenCV.

2. Gathering System Information:

   • Use commands like cv2.getBuildInformation() to confirm OpenCV configuration.
   • Monitor CPU and memory usage during streaming to identify resource bottlenecks.

3. Isolating the Issue:

   • Test each camera separately to determine if one camera is causing more latency than the other.
   • Try different Gstreamer commands to see if they yield better performance.

4. Recommended Fixes:

   • Add sync=0 to the Gstreamer pipeline command to disable synchronization, which has been reported to reduce latency significantly.

     gst-launch-1.0 tcpclientsrc host=192.168.0.52 port=5000 ! gdpdepay ! rtph264depay ! avdec_h264 ! videoconvert ! autovideosink sync=0
     

   • Incorporate drop=1 into the pipeline to manage frames better and reduce crashes.

     gst-launch-1.0 tcpclientsrc host=192.168.0.52 port=5000 ! gdpdepay ! rtph264depay ! h264parse ! nvv4l2decoder ! videoconvert ! autovideosink sync=0 drop=1
     

5. Testing with Different Protocols:

   • Consider switching from TCP to UDP or RTSP for video streaming, as this may alleviate some latency issues.

6. Best Practices for Future Prevention:

   • Regularly update drivers and software libraries (OpenCV, Gstreamer) to ensure compatibility and performance improvements.
   • Implement proper error handling in Python scripts to manage unresponsiveness gracefully.
   • Utilize single-threaded access for multiple camera streams as suggested by users experiencing similar issues.

7. Unresolved Aspects:

   • Further investigation may be needed regarding why OpenCV becomes unresponsive when handling multiple video streams.
   • Users should consider whether hardware limitations of the Jetson Orin Nano contribute to these issues, especially under high load scenarios.

By following these steps and recommendations, users can troubleshoot and potentially resolve latency issues encountered while using Gstreamer with OpenCV on the Nvidia Jetson Orin Nano Dev board effectively.