@ARTICLE{taioli_24_UAVS,
       author={Taioli, Francesco and Giuliari, Francesco and Wang, Yiming and Berra, Riccardo and Castellini, Alberto and Bue, Alessio Del and Farinelli, Alessandro and Cristani, Marco and Setti, Francesco},
       journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
       title={{Unsupervised Active Visual Search with Monte Carlo Planning under Uncertain Detections}}, 
       year={2024},
       volume={46},
       number={12},
       pages={11047-11058},
       doi={10.1109/TPAMI.2024.3451994}
  }

|-- Dockerfile
|-- Makefile
|-- README.md
|-- target
|   |-- pomp_be_pd # executable for the policy computation
|-- input_pomp_be_pd 
|   |-- ... folder containing files needed for the policy
|-- output.txt
|-- src  
|   |-- include
|   |   |-- ... header files
|   |-- *.cpp files

This project was develop using ubuntu 18.04 (boost library version 1.65.1). Setup the project by running the following commands:

1. Setup docker image

    docker build -t pomp_be_pd .
    docker run -it --rm -v "$(pwd):/workspace" --security-opt seccomp=unconfined --name pomp_be_pd_container -w /workspace pomp_be_pd /bin/bash

2. (Optional) Inside the docker image, test the boost library version

    g++ -o boost_version test_boost_library.cpp -L/usr/lib/x86_64-linux-gnu
    ./boost_version # should display 'Boost version: 1.65.1'

3. Compile the project

    cmake --target clean -G "Unix Makefiles" . # generate Makefile
    make -j 8

4. Run the policy solver

    ./target/pomp_be_pd  --problem "activevisualsearch" --runs=1 --maxdoubles=10 --mindoubles=10 --use_gt=1 --penality=1 --accuracy=120 --target_id=3 --startpos=10

The original (without pre-processing) dataset can be found here. For speed purposes, we applied some pre-processing steps to the dataset, thus not every files describe above is used during the simulation phase.

The folder pomp_be_pd contains several files:

• ListView.txt: each rows represent an image of the AVDB datasets.
• MatrixG.txt: each rows index is associated to the same row in ListView.txt. Each column value has a purpose:
  • Col 0: row index if we execute action: rotate_ccw
  • Col 1: row index if we execute action: rotate_cw
  • Col 2: row index if we execute action: forward
  • Col 3: row index if we execute action: backward
  • Col 4: row index if we execute action: deprecated
  • Col 5: row index if we execute action: deprecated
• MatrixL_dt: each rows index is associated to the same row in ListView.txt. Each column i can be 0/1, if object i is detected or not by the object detector.
• MatrixL_gt: same as MatrixL_dt, but contains ground truth annotatations.
• ObservMatrix.txt: take a look at the image Visualize camera positions and directions for an easier explanation. In AVDB, a house (scene) is represented by a grid structure in a 2D plane (cell can thus be inside or outside the house). Every red-dot represent the agent pose inside the house, and every blue lines represent different view-point of the agent. The aim of ObservMatrix.txt is the following: the scene, compose of a grid structure is represented by a matrix, which can contains values 1 or 0, respectively if that particual cell is in the FOV of the agent or not from a particular pose. As usual, each row index of ObservMatrix.txt is associated to ListView.txt. Finally, this Observability matrix is then flattend to a single vector.
• particles_that_are_inside_home.txt: the scene is represented by a 2D grid structure. Aim of this file is to track if a given cell is inside (1) or outside (0) the home.
• GaussObsvMatrix.txt: the idea is similar to ObservMatrix.txt. Here, we save for each pose of the agent, the probabilities to have the target object in location j given the agent pose.
• detector_stat.json: contains the statistics of the object detector (precision and recall for each object)

Download some test samples from G-Drive here. Extract the content inside the input_pomp_be_pd folder. Thus, the folder should contain the following files:

• ListView.txt
• MatrixG.txt
• etc..

Code based on Monte-Carlo Planning in Large POMDPs - NeurIPS