This repo is a ROS Package which can be used for monitoring camera based Human Detection Modules.
-
Position Deviation Estimation (PDE) :
- Takes input of bounding boxes from the human detection model.
- Creates a tracklet of the nearest person from the camera.
- Estimates Deviation in tracklet of the nearest person detection by the human detection model.
- Outputs the Estimated Deviation into a ROS topic (as a
float
message).
-
False Negative Monitor (FNM)
- Takes RGB image input from a camera.
- Classifies negative instances (no detection instances) from the human detection model into False Negative and True Negative.
- Outputs the binary classification into a ROS topic (as a
bool
message).
-
Detection Continuity Checker (DCC) :
- Checks for continuity of output from the detection model.
- Creates an alert notifying the non-continuity in output.
- Changes the Active Monitoring Component between FNM and PDE.
-
The Noetic branch of the repo depends on :
- SENTOR System: https://github.com/lcas/sentor
- SORT Tracker: https://github.com/abewley/sort
-
The SENTOR is used as the DCC for monitoring the human detection ros topic and publishes a boolean ROS message based on the continuity of detections from the human detection mdoel
-
The SORT is used as the tracker in the framework for generating the tacklet of the nearest person infront of the camera.
- To install the framework :
- clone the repo into a ROS workspace
cd /path/to/workspace/src git clone https://github.com/hariharan20/ddom -b noetic
- install the dependencies :
cd /path/to/workspace/src git clone https://github.com/lcas/sentor cd DDOM/tracker/src git clone https://github.com/abewley/sort cd /path/to/workspace/
- build the workspace
catkin_make
- source the workspace
source /path/to/workspace/devel/setup.bash
- Launch the framework :
roslaunch ddom ddom_v2.launch
- Topic names and path to models are set in a config file located at
/path/to/workspace/src/DDOm/ddom/config/config_v2.yaml - Change the config file for custom topic names and camera-specific parameters.
If you find this work useful, please refer to the following citation:
@inproceedings{arunachalam2024runtime,
title={Runtime Anomaly Monitoring of Human Perception Models for Robotic Systems},
author={Arunachalam, H and Huang, Z and Hanheide, M and Guevara, L},
booktitle={2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)},
pages={723--729},
year={2024},
organization={IEEE}
}