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intersection_traffic_meter

README.md

Intersection traffic meter demo

NB: The demo uses YOLOV8 model which takes up to 10-15 minutes to compile to TensorRT engine. The first launch may take a decent time.

The pipeline detects when cars, trucks or buses cross a city intersection delimited by user-configured polygon and the direction of the crossing. The crossing events are attached to individual tracks and are counted for each video source and polygon edge separately; the counters are displayed on the frame. The crossing events are also stored with Graphite and displayed on a Grafana dashboard.

Preview:

Tested on platforms:

  • Nvidia Turing
  • Nvidia Jetson Orin family

Demonstrated operational modes:

  • real-time processing: RTSP streams (multiple sources at once);

Demonstrated adapters:

  • Video loop adapter;
  • Always-ON RTSP sink adapter;

Prerequisites

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.

Build Engines

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

./samples/intersection_traffic_meter/build_engines.sh

Run Demo

# you are expected to be in Savant/ directory

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

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

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

# for pre-configured Grafana dashboard visit
# http://127.0.0.1:3000/d/WM6WimE4z/crossings?orgId=1&refresh=5s

# Ctrl+C to stop running the compose bundle

To create a custom Grafana dashboard, sign in with admin\admin credentials.

Detector Model

This demo uses DeepStream-Yolo to process YOLO models and allows you to use any YOLO model that DeepStream-Yolo supports. The demo uses an already prepared yolov8m. If you are going to use any other model (e.g. custom yolov8m), follow the DeepStream-Yolo export instructions. For example, YOLOv8 instructions are here.

Performance Measurement

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

# you are expected to be in Savant/ directory

mkdir -p data
curl -o data/leeds_1080p.mp4 https://eu-central-1.linodeobjects.com/savant-data/demo/leeds_1080p.mp4

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

./samples/intersection_traffic_meter/run_perf.sh

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