Awesome
boxmot_ros
Docker Usage by adding to compose.yml file
To use GPU with docker while on AMD64 systems, install nvidia-container-toolkit with given instructions.
Supported platforms
Replace image and 'device' parameter in the compose.yml with following values for respective systems.
| System | ROS Version | Value for image | Value for device | Size | file |
|---|---|---|---|---|---|
| AMD64 | Humble | ghcr.io/kalanaratnayake/boxmot-ros:humble | cpu, 0, 0,1,2 | 5.7 GB | docker/compose.amd64.yaml |
Docker Usage with this repository
Clone this reposiotory
mkdir -p boxmot_ws/src && cd boxmot_ws/src
git clone https://github.com/KalanaRatnayake/boxmot_ros.git && cd ..
Pull the Docker image and start compose (No need to run docker compose build)
cd src/boxmot_ros/docker
docker compose -f compose.amd64.yaml pull
docker compose -f compose.amd64.yaml up
Reset the system and remove volume
docker compose -f compose.amd64.yaml down
docker volume rm docker_boxmot
Native Usage
Clone this repository with and install dependencies.
git clone https://github.com/KalanaRatnayake/boxmot_ros.git
git clone https://github.com/KalanaRatnayake/detection_msgs.git
pip3 install -r boxmot_ros/requirements.txt
cd ..
rosdep install --from-paths src -y --ignore-src
Build the package
If required, edit the parameters at `config/boxmot_ros_params.yaml' and then at the workspace root run,
colcon build
Start the system
To use the boxmot tracking, run the launch file in terminal, run,
source ./install/setup.bash
ros2 launch boxmot_ros boxmot.launch.py
Parameter decription
| ROS Parameter | Docker ENV parameter | Default Value | Description |
|---|---|---|---|
| yolo_model | YOLO_MODEL | yolov9t.pt | Model to be used. see [1] for default models and [2] for custom models |
| tracking_model | TRACKING_MODEL | deepocsort | Model to be used for tracking. see [3] for default models |
| reid_model | REID_MODEL | osnet_x0_25_msmt17.pt | Model to be used for reidentification. see [3] for default models |
| subscribe_depth | SUBSCRIBE_DEPTH | False | Whether to subscribe to depth image or not. This will also enable the depth_topic variable which publishes synchronized depth image. Use if having a depth camera. A ApproximateTimeSynchronizer is used to sync RGB and Depth images |
| input_rgb_topic | INPUT_RGB_TOPIC | /camera/color/image_raw | Topic to subscribe for RGB image. Accepts sensor_msgs/Image |
| input_depth_topic | INPUT_DEPTH_TOPIC | /camera/depth/points | Topic to subscribe for Depth image. Accepts sensor_msgs/PointCloud2 |
| publish_annotated_image | PUBLISH_ANNOTATED_IMAGE | False | Whether to publish tracking annotated image, increases callback execution time when set to True |
| rgb_topic | RGB_TOPIC | /boxmot_ros/rgb_image | Topic for publishing synchronized rgb images. uses sensor_msgs/Image |
| depth_topic | DEPTH_TOPIC | /boxmot_ros/depth_image | Topic for publishing synchronized depth images. uses detection_msgs/PointCloud2 |
| annotated_topic | ANNOTATED_TOPIC | /boxmot_ros/annotated_image | Topic for publishing annotated images uses sensor_msgs/Image |
| detailed_topic | DETAILED_TOPIC | /boxmot_ros/tracking_result | Topic for publishing detailed results uses detection_msgs/Detections |
| threshold | THRESHOLD | 0.25 | Confidence threshold for predictions |
| device | DEVICE | '0' | cpu for CPU, 0 for gpu, 0,1,2,3 if there are multiple GPUs |
[1] If the model is available at ultralytics models, It will be downloaded from the cloud at the startup. We are using docker volumes to maintain downloaded weights so that weights are not downloaded at each startup.
[2] Uncomment the commented out YOLO_MODEL parameter line and give the custom model weight file's name as YOLO_MODEL parameter. Uncomment the docker bind entry that to direct to the weights folder and comment the docker volume entry for yolo. Copy the custom weights to the weights folder.
[3] If the reid model is available at MODEL_ZOO, and tracking_model is supported [deepocsort, strongsort, ocsort, bytetrack, botsort, hybridsort, imprassoc]. They will be downloaded from the cloud at the startup. We are using docker volumes to maintain downloaded weights so that weights are not downloaded at each startup. Use the snipped in Default models with Docker Compose
Latency description
Here is a summary of whether supported models work with boxmot_ros node (in docker) on various platforms and the time it takes to execute a single interation of BoxmotROS.image_callback function. Values are measured as an average of 100 executions of the function and Input is a 640x480 RGB image at 30 fps.
Laptop -> Ryzen 9 16 core with RTX3070 mobile GPU with Ubuntu 22.04
| Model | Laptop (amd64) |
|---|---|
deepocsort | 27 ms |
strongsort | 20 ms |
ocsort | 17 ms |
bytetrack | 17 ms |
botsort | 14 ms |