PYTHON LABELED RFS

This repository contains Python implementation of jointglmb GLMB [1] and jointlmb LMB [3]. The implementation are ported from rfs_tracking_toolbox\jointlmb\gms and rfs_tracking_toolbox\jointglmb\gms implemented in Matlab (it was done by Prof. Vo's research group). GLMB is originally, theoretically proposed in [0].

• A detail of how to implement Delta-GLMB (with two separated prediction and update steps) is given [2].
• Adaptive birth is implemented based on [3] (Section Adaptive Birth Distribution), mainly focused on equation (75).
• Sampling solutions (ranked assignments), gibbs_multisensor_approx_cheap is implemented in C++ based on Algorithm 2: MM-Gibbs (Suboptimal) [4].
• Adaptive birth is implemented based on [5] (implemented in C++ based on Algorithm 1 Multi-sensor Adaptive Birth Gibbs Sampler), Gaussian Likelihoods.

[0] Vo, Ba-Tuong, and Ba-Ngu Vo. "Labeled random finite sets and multi-object conjugate priors." IEEE Transactions on Signal Processing 61, no. 13 (2013): 3460-3475.
[1] Vo, Ba-Ngu, Ba-Tuong Vo, and Hung Gia Hoang. "An efficient implementation of the generalized labeled multi-Bernoulli filter." IEEE Transactions on Signal Processing 65, no. 8 (2016): 1975-1987.
[2] Vo, Ba-Ngu, Ba-Tuong Vo, and Dinh Phung. "Labeled random finite sets and the Bayes multi-target tracking filter." IEEE Transactions on Signal Processing 62, no. 24 (2014): 6554-6567.
[3] Reuter, Stephan, Ba-Tuong Vo, Ba-Ngu Vo, and Klaus Dietmayer. "The labeled multi-Bernoulli filter." IEEE Transactions on Signal Processing 62, no. 12 (2014): 3246-3260.
[4] 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.
[5] Trezza, A., Bucci Jr, D. J., & Varshney, P. K. (2021). Multi-sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking. arXiv preprint arXiv:2109.04355.

Our Implementation for Visual GLMB in 2D/3D multi-object tracking

• Our implementation for 2D multi-object tracking with re-identification is released at VisualRFS
• Our implementation for 3D multi-camera multi-object tracking with re-identification is released at 3D-Visual-MOT

USAGE

GLMB

• Original Matlab source: jointglmb_gms_matlab
• Original Python porting: jointglmb_gms_python
• Improved version (code optimized, adaptive birth): jointglmb_gms_python_fast

LMB

• Original Matlab source: jointlmb_gms_matlab
• Original Python porting: jointlmb_gms_python
• Improved version (code optimized): jointlmb_gms_python_fast
• No adaptive birth is implemented for simplification (but can be implemented similar to jointglmb)

Gibbs Sampling

• Python package for an efficient algorithm for truncating the GLMB filtering density based on Gibbs sampling.
• The implementation is done in C++ and based on Algorithm 1. Gibbs (and "Algorithm 1a") of paper [1].
• Python wrapper for faster computation

MS-GLMB

• gibbs_multisensor_approx_cheap is implemented in C++.
• Adaptive birth is implemented in C++, Gaussian Likelihoods.

Contact

Linh Ma (linh.mavan@gm.gist.ac.kr), Machine Learning & Vision Laboratory, GIST, South Korea

Citation

If you find this project useful in your research, please consider citing by:

@article{van2024visual,
      title={Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets}, 
      author={Linh~Van~Ma and Tran~Thien~Dat~Nguyen and Changbeom~Shim and Du~Yong~Kim and Namkoo~Ha and Moongu~Jeon},
      journal={Pattern Recognition},
      volume = {156},
      year={2024},
      publisher={Elsevier}
}

@article{linh2024inffus,
      title={Track Initialization and Re-Identification for {3D} Multi-View Multi-Object Tracking}, 
      author={Linh Van Ma, Tran Thien Dat Nguyen, Ba-Ngu Vo, Hyunsung Jang, Moongu Jeon},
      journal={Information Fusion},
      volume = {111},
      year={2024},
      publisher={Elsevier}
}