Implementation of several Bayesian multi-target tracking algorithms, including Poisson multi-Bernoulli mixture filters for sets of targets and sets of trajectories. The repository also includes the GOSPA metric and a metric for sets of trajectories to evaluate performance.

Related tags

Deep LearningMTT
Overview
This repository contains the Matlab implementations for the following multi-target filtering/tracking algorithms:

- Folder PMBM contains the implementations of the Poisson multi-Bernoulli mixture (PMBM) filter [1][2], the multi-Bernoulli mixture (MBM) filter [3], and (track-oriented) Poisson multi-Bernoulli (PMB) [1].


In order to run the filters, execute PMBMtarget_filter.m for the PMBM filter MBMtarget_filter.m for the MBM filter

PMBMtarget_filter_tracks_all.m runs the PMBM filter with sequential track formation, linking target states estimates from the same Bernoulli component, which is uniquely identified by a start time and measurement. This information can be made explicit in the posterior via auxiliary variables [4]. Note that Bayesian track formation is obtained via densities on sets of trajectories, not linking target state estimates [5].

- Folder CD MTT filters contains the implementations of the continuous-discrete PMBM, continuous-discrete PHD, and continuous-discrete CPHD filters described in [6].

- Folder TPHD contains the implementations of the trajectory probability hypothesis density (TPHD) filter and the trajectory cardinality PHD (TCPHD) filter for sets of trajectories in [7].

In order to run the filters, execute GM_TPHD_filter.m and GM_TCPHD_filter.m

- Folder TPMBM filter contains the implementations of the trajectory PMBM (TPMBM) filter [8][9], trajectory MBM (TMBM) filter [10], trajectory PMB (TPMB) filter [4] and trajectory MB (TMB) filter [11]. Each of these filters can be run to estimate the set of alive trajectories or the set of all trajectories at each time step (running a different file).

- Folder OOS TPMBM filter contains the implementations of the continuous-discrete TPMBM and continuous-discrete  TPMB filters with out-of-sequence measurements [16].


- Evaluation of the multi-target filters is based on the generalised optimal subpattern-assignment (GOSPA) and its decomposition into localisation errors for properly detected targets, and costs for false and missed targets  [12][13][14].


- Evaluation of multi-target trackers (filters that estimate a set of trajectories) is based on the LP trajectory metric for sets of trajectories and its decomposition into localisation errors for properly detected targets, and costs for false, missed targets, and track switches [15].


- Open access versions of the above papers can be found in https://www.liverpool.ac.uk/electrical-engineering-and-electronics/staff/angel-garcia-fernandez/publications/

- A relevant online course on multiple target tracking is provided here:

https://www.youtube.com/channel/UCa2-fpj6AV8T6JK1uTRuFpw

REFERENCES

[1] J. L. Williams, "Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member," in IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 3, pp. 1664-1687, July 2015.

[2] A. F. García-Fernández, J. L. Williams, K. Granström, and L. Svensson, “Poisson multi-Bernoulli mixture filter: direct derivation and implementation,” IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 4, pp. 1883–1901, Aug. 2018.

[3] A. F. García-Fernández, Y. Xia , K. Granström, L. Svensson, J. L. Williams, "Gaussian implementation of the multi-Bernoulli mixture filter", in Proceedings of the 22nd International conference on Information Fusion, 2019.

[4] Á. F. García-Fernández, L. Svensson, J. L. Williams, Y. Xia and K. Granström, "Trajectory Poisson Multi-Bernoulli Filters," in IEEE Transactions on Signal Processing, vol. 68, pp. 4933-4945, 2020.

[5] Á. F. García-Fernández, L. Svensson and M. R. Morelande, "Multiple Target Tracking Based on Sets of Trajectories," in IEEE Transactions on Aerospace and Electronic Systems, vol. 56, no. 3, pp. 1685-1707, June 2020.

[6] A. F. García-Fernández, S. Maskell, "Continuous-discrete multiple target filtering: PMBM, PHD and CPHD filter implementations," IEEE Transactions on Signal Processing, vol. 68, pp. 1300-1314, 2020.

[7] A. F. García-Fernández and L. Svensson, “Trajectory PHD and CPHD filters”, IEEE Transactions on Signal Processing, vol. 67, no. 22, pp. 5702-5714,Nov. 2019.

[8] K. Granström, L. Svensson, Y. Xia, J. Williams and Á. F. García-Fernández, "Poisson Multi-Bernoulli Mixture Trackers: Continuity Through Random Finite Sets of Trajectories," 2018 21st International Conference on Information Fusion (FUSION), Cambridge, 2018.

[9] K. Granström, L. Svensson, Y. Xia, J. Williams and Á. F. García-Fernández, "Poisson Multi-Bernoulli Mixtures for Sets of Trajectories," https://arxiv.org/abs/1912.08718

[10] Y. Xia, K. Granström, L. Svensson, A. F. García-Fernández, and J. L. Wlliams, “Multi-scan implementation of the trajectory Poisson multi-Bernoulli mixture filter,” Journal of Advances in Information Fusion. Special Issue on Multiple Hypothesis Tracking., vol. 14, no. 2, pp. 213–235, Dec. 2019.

[11] A. F. García-Fernández, L. Svensson, J. L. Williams, Y. Xia, K. Granström,  “Trajectory multi-Bernoulli filters for multi-target tracking based on sets of trajectories” in 23rd International Conference on Information Fusion, 2020.

[12] A. S. Rahmathullah, A. F. García-Fernández, and L. Svensson, “Generalized optimal sub-pattern assignment metric,” in 20th International Conference on Information Fusion, 2017.

[13] A. F. García-Fernández, and L. Svensson, "Spooky effect in optimal OSPA estimation and how GOSPA solves it," in 22nd International Conference on Information Fusion, 2019.

[14] L. Svensson, Presentation on GOSPA: https://www.youtube.com/watch?v=M79GTTytvCM

[15] Á. F. García-Fernández, A. S. Rahmathullah and L. Svensson, "A Metric on the Space of Finite Sets of Trajectories for Evaluation of Multi-Target Tracking Algorithms," in IEEE Transactions on Signal Processing, vol. 68, pp. 3917-3928, 2020.

[16] Á. F. García-Fernández and W. Yi, "Continuous-Discrete Multiple Target Tracking With Out-of-Sequence Measurements," in IEEE Transactions on Signal Processing, vol. 69, pp. 4699-4709, 2021






Owner
Ángel García-Fernández
Lecturer, University of Liverpool
Ángel García-Fernández
Official PyTorch Implementation of GAN-Supervised Dense Visual Alignment

GAN-Supervised Dense Visual Alignment — Official PyTorch Implementation Paper | Project Page | Video This repo contains training, evaluation and visua

944 Jan 07, 2023
I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive constraining

I-SECRET This is the implementation of the MICCAI 2021 Paper "I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive con

13 Dec 02, 2022
Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation

CaloGAN Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks. This repository c

Deep Learning for HEP 101 Nov 13, 2022
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

107 Dec 02, 2022
Pytorch implementation of Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization https://arxiv.org/abs/2008.11646

[TCSVT] Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization LPN [Paper] NEWs Prerequisites Python 3.6 GPU Memory = 8G Numpy 1.

46 Dec 14, 2022
Replication of Pix2Seq with Pretrained Model

Pretrained-Pix2Seq We provide the pre-trained model of Pix2Seq. This version contains new data augmentation. The model is trained for 300 epochs and c

peng gao 51 Nov 22, 2022
ShapeGlot: Learning Language for Shape Differentiation

ShapeGlot: Learning Language for Shape Differentiation Created by Panos Achlioptas, Judy Fan, Robert X.D. Hawkins, Noah D. Goodman, Leonidas J. Guibas

Panos 32 Dec 23, 2022
Optimal space decomposition based-product quantization for approximate nearest neighbor search

Optimal space decomposition based-product quantization for approximate nearest neighbor search Abstract Product quantization(PQ) is an effective neare

Mylove 1 Nov 19, 2021
[Preprint] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

Chasing Sparsity in Vision Transformers: An End-to-End Exploration Codes for [Preprint] Chasing Sparsity in Vision Transformers: An End-to-End Explora

VITA 64 Dec 08, 2022
AWS provides a Python SDK, "Boto3" ,which can be used to access the AWS-account from the local.

Boto3 - The AWS SDK for Python Boto3 is the Amazon Web Services (AWS) Software Development Kit (SDK) for Python, which allows Python developers to wri

Shreyas Srivastava 1 Oct 25, 2021
This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

TransUNet This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Usage

1.4k Jan 04, 2023
Contra is a lightweight, production ready Tensorflow alternative for solving time series prediction challenges with AI

Contra AI Engine A lightweight, production ready Tensorflow alternative developed by Styvio styvio.com » How to Use · Report Bug · Request Feature Tab

styvio 14 May 25, 2022
A module for solving and visualizing Schrödinger equation.

qmsolve This is an attempt at making a solid, easy to use solver, capable of solving and visualize the Schrödinger equation for multiple particles, an

506 Dec 28, 2022
BLEURT is a metric for Natural Language Generation based on transfer learning.

BLEURT: a Transfer Learning-Based Metric for Natural Language Generation BLEURT is an evaluation metric for Natural Language Generation. It takes a pa

Google Research 492 Jan 05, 2023
“Robust Lightweight Facial Expression Recognition Network with Label Distribution Training”, AAAI 2021.

EfficientFace Zengqun Zhao, Qingshan Liu, Feng Zhou. "Robust Lightweight Facial Expression Recognition Network with Label Distribution Training". AAAI

Zengqun Zhao 119 Jan 08, 2023
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

Adversrial Machine Learning Benchmarks This code belongs to the papers: Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? Det

Adversarial Machine Learning 9 Nov 27, 2022
LSTM Neural Networks for Spectroscopic Studies of Type Ia Supernovae

Package Description The difficulties in acquiring spectroscopic data have been a major challenge for supernova surveys. snlstm is developed to provide

7 Oct 11, 2022
DABO: Data Augmentation with Bilevel Optimization

DABO: Data Augmentation with Bilevel Optimization [Paper] The goal is to automatically learn an efficient data augmentation regime for image classific

ElementAI 24 Aug 12, 2022
Data manipulation and transformation for audio signal processing, powered by PyTorch

torchaudio: an audio library for PyTorch The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the

1.9k Dec 28, 2022
Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch

Who has never met a forged picture on the web ? No one ! Everyday we are constantly facing fake pictures touched up in Photoshop but it is not always

Rony Abecidan 77 Dec 16, 2022