Object Detection and Tracking on Jetson Orin Nano

Issue Overview

The user is seeking to implement real-time object detection and tracking for approximately 20 objects in a video stream. The specific requirements include:

• Tracking about 20 objects simultaneously
• Real-time processing at 30 frames per second
• Minimum video resolution of 1920 x 1080 (Full HD)
• Objects have unique black letters/numbers on white backgrounds
• Each object’s identifier is 45×45 pixels in size
• Preference for using Python programming language

The user is considering using the Nvidia Jetson Orin Nano development board for this task, as they believe it might be beyond the capabilities of a Raspberry Pi or mini-PC.

Possible Causes

While this is not an issue per se, there are several factors that could affect the feasibility and performance of the proposed object detection and tracking system:

1. Hardware limitations: The chosen hardware platform may not have sufficient processing power or GPU capabilities to handle real-time object detection at the required resolution and frame rate.

2. Software optimization: Inefficient algorithms or unoptimized code could lead to poor performance, even on capable hardware.

3. Object detection model selection: Choosing an inappropriate or overly complex model could result in slower processing times.

4. Camera and input stream issues: Low-quality or poorly configured video input could affect the system’s ability to detect and track objects accurately.

5. Lighting and environmental factors: Suboptimal lighting conditions or visual obstructions could impact the system’s ability to detect and track objects reliably.

Troubleshooting Steps, Solutions & Fixes

To implement the desired object detection and tracking system on the Nvidia Jetson Orin Nano, consider the following steps and solutions:

1. Confirm hardware compatibility:
   The Nvidia Jetson Orin Nano is indeed capable of handling this task. It’s designed for AI and computer vision applications, making it suitable for real-time object detection and tracking.

2. Use appropriate object detection framework:
   Utilize NVIDIA’s jetson-inference library, which is optimized for Jetson devices. This library provides pre-trained models and examples for object detection.

3. Implement detection using DetectNet:
   Use the detectNet class from the jetson-inference library. This class is designed for object detection and can handle multiple objects in a single frame.

4. Set up the project:
   Clone the jetson-inference repository and follow the setup instructions provided in the documentation.

5. Customize the detection model:
   Since you have a specific use case with unique identifiers, you may need to train a custom model or fine-tune an existing one to recognize your 20 specific objects.

6. Optimize for performance:

   • Use TensorRT for model optimization on the Jetson platform.
   • Experiment with different pre-trained models to find the best balance between accuracy and speed.
   • Consider using a smaller input resolution if full HD proves too demanding for real-time performance.

7. Implement in Python:
   Use the Python API provided by jetson-inference. Here’s a basic example of how to set up object detection:

   import jetson.inference
   import jetson.utils
   
   net = jetson.inference.detectNet("ssd-mobilenet-v2", threshold=0.5)
   camera = jetson.utils.videoSource("csi://0")      # '/dev/video0' for V4L2
   display = jetson.utils.videoOutput("display://0") # 'my_video.mp4' for file
   
   while display.IsStreaming():
       img = camera.Capture()
       detections = net.Detect(img)
       display.Render(img)
       display.SetStatus("Object Detection | Network {:.0f} FPS".format(net.GetNetworkFPS()))
   

8. Adapt the code for your use case:

   • Modify the detection threshold as needed.
   • Implement logic to track objects across frames if required.
   • Add code to process the unique identifiers (letters/numbers) on each object.

9. Monitor system performance:
   Use Jetson’s built-in tools to monitor CPU, GPU, and memory usage to ensure the system is not overloaded.

10. Optimize camera settings:
    Ensure your camera is capable of streaming at 1080p30 and is properly configured for optimal performance.

11. Consider environmental factors:
    Set up proper lighting conditions to ensure the black letters/numbers on white backgrounds are clearly visible to the camera.

By following these steps and utilizing the resources provided by NVIDIA for the Jetson platform, you should be able to implement your object detection and tracking system on the Jetson Orin Nano. Remember to test thoroughly and iterate on your solution to achieve the desired performance and accuracy.