AI Based Object Redaction - Performance

AI Based Object Redaction
Overview
Architecture Supported Platforms Capabilities
Getting Started
Dependencies Evaluating librrobjectredaction Building librrobjectredaction
GStreamer Plugin
rrfacedetector rrobjectredactor
Examples
Library Examples GStreamer Pipelines
Performance
Jetson Xavier NX Jetson Xavier AGX
Troubleshooting
FAQ
Contact Us

The library has four major consuming methods: convert the input buffer to the desired format and size of the AI model, the detection of the desired object done by the ONNX model, tracking and the application of the redaction effect.

For testing purposes, take into account the following points:

• Jetpack 5.1.X
• Tested with GPU and CPU only. For GPU, execution is done with TensorRT and CUDA.
• The library offers two approaches: each method used separately and all together using the apply method.
• The metrics measured are CPU and GPU consumption, processing time, and FPS.
• The tracker is implemented only with support for CPU.

Check the following links for performance:

• [Xavier NX]

• [Xavier AGX]