Loading repository data…
Loading repository data…
linh-gist / repository
This repository includes Python implementations adapted from Matlab, originally developed by Prof. Ba-Tuong Vo. The original Matlab codes can be found at Prof. Vo's website (https://ba-tuong.vo-au.com/codes.html) or on GitHub (https://github.com/nguyenvanhoa89/tracking/tree/master/Vo_Codes).
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
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].
gibbs_multisensor_approx_cheap is implemented in C++ based on Algorithm 2: MM-Gibbs (Suboptimal) [4].[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.
GLMB
jointglmb_gms_matlabjointglmb_gms_pythonjointglmb_gms_python_fastLMB
jointlmb_gms_matlabjointlmb_gms_pythonjointlmb_gms_python_fastGibbs Sampling
MS-GLMB
gibbs_multisensor_approx_cheap is implemented in C++.Linh Ma (linh.mavan@gm.gist.ac.kr), Machine Learning & Vision Laboratory, GIST, South Korea
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}
}