Real-time multi-modal multi-pedestrian detection and tracking framework.
The main goal of this repository is to provide a clean, and easy to integrate inference pipeline for 2D (and 3D) detection and tracking. Our proposed framework is depicted in fig.1 using five RGB-D cameras.
Fig.1. A schematic the perception framework. Blocks with dashed border will be implemented in a future version
This framework uses a 2D object detector that processes the RGB images from five RGB cameras and outputs 2D bounding boxes. We employed the state-of-the-art YOLOv7 network, as our 2D object detector. YOLOv7 is the powerful object detection algorithm and is perfect for fast real-time applications.
Real-Time 2D detection
The pipeline not only detects but also tracks the location (and velocity) of persons. However, RGB images do not contain depth information, so we use the depth image to translate these 2D bounding boxes to 3D. Obtaining accurate depth information is essential in order to translate 2D object detections into 3D. We utilize a standard feature of the realsense-d455 RGB-D cameras that aligns the depth images with their corresponding RGB images. This alignment allows us to directly extract the region of interest (RoI) in the depth image by examining the bounding boxes provided by the 2D object detector
5X intel realsense d455 1X lidar velodyne VLP-16 1 X 3.1 USB Hub 5 ports
Fig.2. A LiDAR and Cameras mount
In this version the sensor calibration was made manually.
ROS dependencies
The detection pipeline interfaces with ROS and uses some non-default msg types to do this. Primarily the jsk_recognition_msgs and spencer_tracking_msgs are used for detection and tracking respectively.
The jsk_recognition_msgs can be installed using:
sudo apt install ros-${ROS_DISTRO}-jsk-recognition-msgs
NOTE: make sure you have sourced ROS, otherwise
${ROS_DISTRO}will be empty.
Unfortunately spencer_tracking_msgs is not available to install through aptitude (ubuntu's package manager).
The current work around is to build these msgs from their source directories:
cd <your catkin workspace>
git clone [email protected]:spencer-project/spencer_messages.git
git clone [email protected]:srl-freiburg/spencer_tracking_rviz_plugin.git
catkin build
Python dependencies
Simply install this repository with pip (or your preferred python package manager).
pip install -e .Make sure you have all 5 realsense cameras connected and their id's are set correctly in scripts/tudelft_amr_detection.py. Additionally make sure you have sourced your ROS environment, and depending on which onnxruntime execution_provider you have chosen, make sure the corresponding drivers are installed (cuda, tensorrt, openvino, etc.).
Finally simply execute the script.
cd amr_perception/scripts
python tudelft_amr_detection.py
python tudelft_amr_tracking.py