Object tracking algorithm based on cloud of points using segmentation masks. It was improved in case of speed using GPU-based preprocessing and numba-based assinging algorithm.
This branch works with PyTorch 1.10.0.
💪Introduction | 🛠️Installation | 🏃Run | 👀Contents | 🔖Docs
git-lfs docker nvidia-jetpack-4.5 (L4T R32.5.0) #if you are using NVIDIA edge devices
git clone --branch indexing_fast https://gitlab.com/sdbcs-nio3/itl_mipt/segm_tracking/alg/tracking/pointtrack.git cd pointtrack git lfs fetch && git lfs pull source docker/docker_names.sh sh docker/build.sh sh docker/start_devel.sh
See additional information in docs/docker.md
After that you will be in project directory. You will need to register the package manually using scripts below.
ROS package installation
cd .. git clone https://gitlab.com/sdbcs-nio3/itl_mipt/ros_common/camera_objects_msgs.git cd .. source /opt/ros/noetic/setup.bash catkin_make source devel/setup.bash
Train environment installation
pip install -r requirements/train.txt
Run the project
roslaunch pointtrack main.launch \ camera_ns:=/stereo/left \ # camera namespace image_topic:=image_rect \ # colored image topic objects_topic:=objects \ # topic with detection results (see camera object msgs) objects_track_ids_topic:=track_ids \ # ouput topic name print_stats:=1 \ # print or dont print stat params stats_rate:=20 # how often will the information be printed
To train/test a model
See additional information in docs/point_track.md
There were a lot of research on speedup vs quality of work. The original network structure was changed, accelerated using torch2trt. The dependence of the inference speed on the size of segmentation masks is investigated. Also the model has also been accelerated using Numba library. You can also set the maximum number of objects during tracking in order to accurately meet the allocated inference time. All of this improvements explained in docs/point_track_improvements.md
├── docker <- Docker scripts and env setup. ├── docs <- Markdown files which provides an additional information about package. ├── launch <- Launch file for package params in the ROS namespace. ├── requirements <- Directory with main requirements for train/infer stage. ├── scripts <- Scripts for configuration, downloading weights. │ │ │ ... │ ├── ros_node.py <- Entrypoint ros node file (inference). │ ├── train_tracker_with_val.py <- Entrypoint model file (training). │ └── test_tracking.py <- Entrypoint model file (testing). │ ... │ ├── weights <- pth-like files for the model storing in git lfs. ├── README.md <- You are here. ├── package.xml <- Main info about the package for ROS. ├── config.yaml <- Main config for running the ROS node. └── requirements.txt <- Required libraries.