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DDOM: Data Driven Online Monitoring framework for monitoring human detection models. The repo is a ROS Package.

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DDOM (Data Driven Online Monitoring) System

Description

This repo is a ROS Package which can be used for monitoring camera based Human Detection Modules.

Framework

Functioning

1) The Framework consists of three components

  • 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.

Dependency

  • The Noetic branch of the repo depends on :

  • 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.

INSTALLATION AND USAGE

  • 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}
}

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DDOM: Data Driven Online Monitoring framework for monitoring human detection models. The repo is a ROS Package.

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