USB Camera Freezing Issue on Jetson Orin Nano 8GB

Issue Overview

Users are experiencing video freezing issues with the Jetson Orin Nano 8GB when using a Sony FCB EV9500L USB Camera. The problem manifests as follows:

• Initially, the video plays normally
• After some time, the video freezes with a "select timeout warn" message
• The issue occurs when using OpenCV with V4L2 as the backend
• The problem persists even when using GStreamer instead of V4L2
• Video output freezes particularly when sending frames for number plate recognition
• The backend continues to process images and read number plates, but the frontend video stops
• With V4L2, the freezing occurs randomly, even without sending frames for image processing

Possible Causes

1. OpenCV Software Stack Issues: The problem seems to occur within the OpenCV software stack, potentially due to blocking operations or inefficient frame processing.

2. Resource Contention: The system may be struggling to handle both video capture and image processing tasks simultaneously, leading to resource conflicts.

3. Driver Incompatibility: There might be compatibility issues between the USB camera driver and the Jetson Orin Nano’s software stack.

4. Memory Management: Inefficient memory handling in the Python code could lead to resource exhaustion over time.

5. GStreamer Pipeline Configuration: The GStreamer pipeline might not be optimized for real-time video processing and frame extraction.

6. OpenCV Build Configuration: The custom build of OpenCV (version 4.5.4 with CUDA support) might have introduced incompatibilities or performance issues.

Troubleshooting Steps, Solutions & Fixes

1. Isolate the Issue:

   • Test the camera using the GStreamer command-line tool to rule out hardware problems:

     gst-launch-1.0 v4l2src device=/dev/video0 ! video/x-raw,width=1920,height=1080,framerate=30/1 ! videoconvert ! fakesink sync=0
     

2. Optimize GStreamer Pipeline:

   • Modify the GStreamer pipeline to include appsink with synchronization disabled and frame dropping enabled:

     pipeline = "gst-launch-1.0 v4l2src device=/dev/video0 ! video/x-raw,width=1920,height=1080,framerate=30/1 ! videoconvert ! appsink sync=0 drop=1"
     

3. Simplify the Code:

   • Test with a simplified Python script to isolate the problem:

     import cv2
     
     pipeline = "v4l2src device=/dev/video0 ! video/x-raw, width=1920, height=1080, framerate=30/1 ! videoconvert ! appsink"
     cap = cv2.VideoCapture(pipeline, cv2.CAP_GSTREAMER)
     
     while True:
         ret, frame = cap.read()
         if not ret:
             break
         cv2.imshow('frame', frame)
         if cv2.waitKey(1) & 0xFF == ord('q'):
             break
     
     cap.release()
     cv2.destroyAllWindows()
     

4. Optimize Thread Management:

   • Implement a producer-consumer pattern using separate threads for video capture and processing to reduce blocking operations.

5. Memory Management:

   • Implement proper memory cleanup and resource release in your Python code.
   • Consider using context managers (with statements) for resource handling.

6. OpenCV Rebuild:

   • If issues persist, consider rebuilding OpenCV without custom modifications:

     sudo apt-get update
     sudo apt-get install -y build-essential cmake pkg-config libjpeg-dev libtiff5-dev libpng-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev libgtk-3-dev libatlas-base-dev gfortran python3-dev
     
     git clone https://github.com/opencv/opencv.git
     cd opencv
     mkdir build && cd build
     cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_CUDA=ON -D CUDA_ARCH_BIN=7.2 -D CUDA_ARCH_PTX="" -D WITH_CUBLAS=ON -D ENABLE_FAST_MATH=ON -D CUDA_FAST_MATH=ON -D ENABLE_NEON=ON -D WITH_LIBV4L=ON -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF -D WITH_QT=OFF -D WITH_OPENGL=ON ..
     make -j$(nproc)
     sudo make install
     

7. Consider DeepStream SDK:

   • If the primary use case involves deep learning inference, consider using NVIDIA’s DeepStream SDK, which is optimized for real-time video analytics on Jetson platforms.

8. Driver and Firmware Updates:

   • Ensure that both the Jetson Orin Nano and the Sony FCB EV9500L USB Camera have the latest firmware and driver updates installed.

9. Performance Monitoring:

   • Use system monitoring tools like htop or nvidia-smi to check for CPU, GPU, or memory bottlenecks during video processing.

10. Error Logging:

    • Implement comprehensive error logging in your Python script to capture any exceptions or warnings that might provide more insight into the freezing issue.

If these solutions do not resolve the issue, consider reaching out to NVIDIA’s developer support or the Jetson community forums for more specialized assistance, as the problem may be related to specific hardware or software configurations of the Jetson Orin Nano 8GB.