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
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.
Overview
Repo for the Video Person Clustering dataset, and code for the associated paper
Video Person Clustering Repo for the Video Person Clustering dataset, and code for the associated paper. This reporsitory contains the Video Person Cl
source code for https://arxiv.org/abs/2005.11248 "Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics"
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics This work will be published in Nature Biomedical
Learning Features with Parameter-Free Layers (ICLR 2022)
Learning Features with Parameter-Free Layers (ICLR 2022) Dongyoon Han, YoungJoon Yoo, Beomyoung Kim, Byeongho Heo | Paper NAVER AI Lab, NAVER CLOVA Up
EsViT: Efficient self-supervised Vision Transformers
Efficient Self-Supervised Vision Transformers (EsViT) PyTorch implementation for EsViT, built with two techniques: A multi-stage Transformer architect
In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results from as little as 16 seconds of target data.
Neural Instrument Cloning In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results fro
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference
RawVSR This repo contains the official codes for our paper: Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference Xiaoh
🚩🚩🚩
My CTF Challenges 2021 AIS3 Pre-exam / MyFirstCTF Name Category Keywords Difficulty ⒸⓄⓋⒾⒹ-①⑨ (MyFirstCTF Only) Reverse Baby ★ Piano Reverse C#, .NET ★
Baseline of DCASE 2020 task 4
Couple Learning for SED This repository provides the data and source code for sound event detection (SED) task. The improvement of the Couple Learning
Experiments with the Robust Binary Interval Search (RBIS) algorithm, a Query-Based prediction algorithm for the Online Search problem.
OnlineSearchRBIS Online Search with Best-Price and Query-Based Predictions This is the implementation of the Robust Binary Interval Search (RBIS) algo
Google Recaptcha solver.
byerecaptcha - Google Recaptcha solver. Model and some codes takes from embium's repository -Installation- pip install byerecaptcha -How to use- from
PyTorch ,ONNX and TensorRT implementation of YOLOv4
PyTorch ,ONNX and TensorRT implementation of YOLOv4
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions Accepted by AAAI 2022 [arxiv] Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jia
Recognize Handwritten Digits using Deep Learning on the browser itself.
MNIST on the Web An attempt to predict MNIST handwritten digits from my PyTorch model from the browser (client-side) and not from the server, with the
Official PyTorch Implementation of Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition, ICCV 2021
Official PyTorch Implementation of Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition, ICCV 2021
The official PyTorch code implementation of "Human Trajectory Prediction via Counterfactual Analysis" in ICCV 2021.
Human Trajectory Prediction via Counterfactual Analysis (CausalHTP) The official PyTorch code implementation of "Human Trajectory Prediction via Count
Simple Python project using Opencv and datetime package to recognise faces and log attendance data in a csv file.
Attendance-System-based-on-Facial-recognition-Attendance-data-stored-in-csv-file- Simple Python project using Opencv and datetime package to recognise
[MICCAI'20] AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes
AlignShift NEW: Code for our new MICCAI'21 paper "Asymmetric 3D Context Fusion for Universal Lesion Detection" will also be pushed to this repository
Consistency Regularization for Adversarial Robustness
Consistency Regularization for Adversarial Robustness Official PyTorch implementation of Consistency Regularization for Adversarial Robustness by Jiho
領域を指定し、キーを入力することで画像を保存するツールです。クラス分類用のデータセット作成を想定しています。
image-capture-class-annotation 領域を指定し、キーを入力することで画像を保存するツールです。 クラス分類用のデータセット作成を想定しています。 Requirement OpenCV 3.4.2 or later Usage 実行方法は以下です。 起動後はマウスクリック4
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
Expressive Body Capture: 3D Hands, Face, and Body from a Single Image [Project Page] [Paper] [Supp. Mat.] Table of Contents License Description Fittin