A simple pipeline that uses standard Nvidia PeopleNet model to detect persons and their faces in the video. The faces are matched versus bodies and blurred with the integrated OpenCV CUDA functionality. There is also a simple unreliable tracker that helps reduce flickering of boxes.

GPU-accelerated blurring made with OpenCV is a killer feature of this demonstration. It enables very fast and efficient face blurring without CPU utilization.

The Green Icon represents how many people with blurred faces in the scene. The Blue Icon represents how many people with blurred faces in the scene.

Preview:

Features:

YouTube Video:

A step-by-step tutorial.

Tested on platforms:

• Nvidia Turing
• Nvidia Jetson Orin family

Demonstrated operational modes:

• real-time processing: RTSP streams (multiple sources at once);
• capacity processing: directory of files (FPS).

Demonstrated adapters:

• RTSP source adapter;
• video file source adapter;
• Always-ON RTSP sink adapter;
• Video/Metadata sink adapter.

git clone https://github.com/insight-platform/Savant.git
cd Savant
git lfs pull
./utils/check-environment-compatible

Note: Ubuntu 22.04 runtime configuration guide helps to configure the runtime to run Savant pipelines.

The demo uses models that are compiled into TensorRT engines the first time the demo is run. This takes time. Optionally, you can prepare the engines before running the demo by using the command:

# you are expected to be in Savant/ directory

./scripts/run_module.py --build-engines samples/peoplenet_detector/module.yml

# you are expected to be in Savant/ directory

# if x86
docker compose -f samples/peoplenet_detector/docker-compose.x86.yml up

# if Jetson
docker compose -f samples/peoplenet_detector/docker-compose.l4t.yml up

# open 'rtsp://127.0.0.1:554/stream/city-traffic' in your player
# or visit 'http://127.0.0.1:888/stream/city-traffic/' (LL-HLS)

# Ctrl+C to stop running the compose bundle

Download the video file to the data folder. For example:

# you are expected to be in Savant/ directory

mkdir -p data && curl -o data/Free_City_Street_Footage.mp4 \
   https://eu-central-1.linodeobjects.com/savant-data/demo/Free_City_Street_Footage.mp4

Now you are ready to run the performance benchmark with the following command:

./samples/peoplenet_detector/run_perf.sh

Note: Change the value of the DATA_LOCATION variable in the run_perf.sh script if you changed the video.