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https://github.com/chickeninvader/multiobjecttracking

https://github.com/chickeninvader/multiobjecttracking

Science Score: 13.0%

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Repository

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  • Host: GitHub
  • Owner: Chickeninvader
  • Language: MATLAB
  • Default Branch: main
  • Size: 1.73 MB
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Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme

README.md

Multi-Object-Tracking

This repository contains MATLAB assignments completed for the course "Multi-Object Tracking for Automotive Systems" offered by EDX Chalmers University of Technology. Each assignment focuses on implementing various tracking algorithms for automotive applications.

Home-Assignment 01 (HA01) - Single-Object Tracking in Clutter

Implemented Algorithms:

  • Nearest Neighbors Filter (NN)
  • Probabilistic Data Association Filter (PDA)
  • Gaussian Sum Filter (GSF)

Home-Assignment 02 (HA02) - Tracking n Objects in Clutter

Implemented Algorithms:

  • Global Nearest Neighbors Filter (GNN)
  • Joint Probabilistic Data Association Filter (JPDA)

- Track-oriented Multiple Hypothesis Tracker (TO-MHT)

Home-Assignment 03 (HA03) - Random Finite Sets

Implemented Algorithms:

  • Probability Hypothesis Density Filter (PHD)
  • Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD)

Home-Assignment 04 (HA04) - MOT Using Conjugate Priors

Implemented Algorithms:

  • Multi-Bernoulli Mixture filter (MBM)
  • Poisson Multi-Bernoulli Mixture filter (PMBM)

Prerequisites

To run the code in this repository, ensure the following: - MATLAB (R2020b or later recommended) - Statistics and Machine Learning Toolbox (if applicable)

Results and Metrics

This section provides a summary of the results obtained from the implemented algorithms, captured in the following files:

  1. Cardinality

    • This file contains the cardinality estimates for each time step, comparing the predicted and ground truth values.
    • Screenshot: Cardinality Results

  2. Metrics

    • Includes performance metrics such as OSPA (Optimal Subpattern Assignment) and GOSPA (Generalized OSPA) scores, highlighting the accuracy of the tracking algorithms.
    • Screenshot: Metrics Results

  3. Nonlinear Prediction Ground Truth

    • This file visualizes the ground truth trajectories versus the predicted trajectories for nonlinear models.
    • Screenshot: Nonlinear Prediction Results

Owner

  • Login: Chickeninvader
  • Kind: user

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