Building a Custom GStreamer Plugin for NVIDIA DeepStream

While NVIDIA DeepStream excels at video analytics, its standard inference plugin struggles with custom models and complex post-processing logic. You can overcome these limitations by building a custom GStreamer plugin in Python that writes detection data directly into DeepStream's shared metadata structure. This powerful technique uses `pyservicemaker` to perform zero-copy inference, feeding GPU video frames directly into your existing PyTorch or TensorRT models. After running inference, the plugin attaches the results by acquiring and populating metadata objects from DeepStream's efficient memory pools. This approach gives you complete control over your AI logic while retaining the full performance of the hardware-accelerated DeepStream pipeline.