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C++ Implementation for MV-GLMB-AB

  • The following functions are implemented in C++ and can be called from Python:
    • gibbs_multisensor_approx_dprobsample
    • MCAdaptiveBirth, Monte Carlo Adaptive Birth implementation for paper "Multi-sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking".
    • bboxes_ioi_xyah_back2front_all, compute Intersection over Area (IOA) from objects closest to the camera.
    • MSGLMB, C++ class for MV-GLMB-AB filter
    • meanShift, clustering algorithm implementation to obtain a cluster of ground plane points that uses to generate new birth targets.
    • KMeans
    • multisensor_lapjv, measurement association for each sensor using LapJV

Requirements

  • Python 3.7
  • C++ compiler (eg. Windows: Visual Studio 15 2017, Ubuntu: g++)
  • pybind11 git submodule add https://github.com/pybind/pybind11.git
  • EigenRand git submodule add https://github.com/bab2min/EigenRand.git
  • Linear Assignment Problem solver https://github.com/gatagat/lap.git

Install

python setup.py build develop

Install Eigen for Windows (after the following steps, add include directory C:\eigen-3.4.0 for example.)

  1. Download Eigen 3.4.0 (NOT lower than this version) from it official website https://eigen.tuxfamily.org/ or ZIP file here.
  2. mkdir build_dir
  3. cd build_dir
  4. cmake ../
  5. make install, this step does not require

Install Eigen for Linux

  1. install and use eigen3 on ubuntu 16.04
  2. sudo apt-get install libeigen3-dev libeigen3-dev is installed install to /usr/include/eigen3/ and /usr/lib/cmake/eigen3.
  3. Thus, we must make a change in CMakeLists.txt SET( EIGEN3_INCLUDE_DIR "/usr/local/include/eigen3" ) to SET( EIGEN3_INCLUDE_DIR "/usr/include/eigen3/" ).

Paper References

  • Vo, B. N., Vo, B. T., & Beard, M. (2019). Multi-sensor multi-object tracking with the generalized labeled multi-Bernoulli filter. IEEE Transactions on Signal Processing, 67(23), 5952-5967.
  • Ong, J., Vo, B. T., Vo, B. N., Kim, D. Y., & Nordholm, S. (2020). A bayesian filter for multi-view 3D multi-object tracking with occlusion handling. IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • Trezza, A., Bucci, D. J., & Varshney, P. K. (2022). Multi-sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking. IEEE Transactions on Signal Processing, 70, 1010-1025.
  • Cheng, Y. (1995). Mean shift, mode seeking, and clustering. IEEE transactions on pattern analysis and machine intelligence, 17(8), 790-799.

Contact

Linh Ma ([email protected]), Machine Learning & Vision Laboratory, GIST, South Korea