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Real-time Crowd Segmentation from 2D/3D lidar person detections

Simulating crowds of people in Isaac Sim

This repository...

If you find this code relevant for your work, please consider citing one or both of these papers. A bibtex entry is provided below:

@article{hughes2023foundations,
title={TODO},
author={TODO},
year={2024},
eprint={xxxx.xxxxx},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

Installation

Requirements

  • GPU for machine learning
    • When not using CUDA install pytorch manually!

Setup

  1. Create a virtual python environment with python3 -m venv env
  2. Enter into the environment using source env/bin/activate
  3. Install required packages pip install -r requirements.txt

Configuration

Most basic configurations for both input/label generation and the scene segmentation itself can be found in the config folder.

Run

Input and Label Generator

Requirements

Have detection data in either CrowdBot or JRDB (with additional velocities of all people) format. See this repository for a simulator to create data in JRDB format.

Usage with JRDB based data

Run python3 generator/generator.py --dataformat "JRDB" with the following additional arguments.

  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.

Usage with CrowdBot data

This requires a few more arguments

Run python3 generator/generator.py --dataformat "CrowdBot" with the following additional arguments.

  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
  • --input_tf with the path to the file with all tf frames.
  • --input_vel with the path to the file with all detection velocities.

Additionally the argument --label "False" can be added for both cases to only generate the input without generating all the label.

Scene segmentation

Train

  1. Create a text file at the location given by data_root in the config/config.yml file. This file should have all relative paths to the training data folder.

Optionally: The same can be done for the validation data using validation_dir as the config variable

  1. Run python3 engine/train.py to start training of the model.

Predict

Run python3 engine/predict.py with the following arguments.

  • --input with the location to the input images you want to predict
  • --output with the location where the predicted segmentations should be stored
  • --checkpoint with the path to the checkpoint file to restart at. If empty it will retrain with the given data.

About

Segmentation of crowded spaces, with conversion from 3D lidar detections to visual scene descriptors

Resources

Readme
GPL-3.0 license
Activity
Custom properties

Stars

0 stars

Watchers

1 watching

Forks

1 fork
Report repository

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Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Real-time Crowd Segmentation from 2D/3D lidar person detections
Simulating crowds of people in Isaac Sim
This repository...
If you find this code relevant for your work, please consider citing one or both of these papers. A bibtex entry is provided below:
@article{hughes2023foundations,
title={TODO},
author={TODO},
year={2024},
eprint={xxxx.xxxxx},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

Installation
Requirements
  • GPU for machine learning

    • When not using CUDA install pytorch manually!
Setup
  1. Create a virtual python environment with python3 -m venv env
  2. Enter into the environment using source env/bin/activate
  3. Install required packages pip install -r requirements.txt
Configuration
Most basic configurations for both input/label generation and the scene segmentation itself can be found in the config folder.
Run
Input and Label Generator
Requirements
Have detection data in either CrowdBot or JRDB (with additional velocities of all people) format. See this repository for a simulator to create data in JRDB format.
Usage with JRDB based data
Run python3 generator/generator.py --dataformat "JRDB" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
Usage with CrowdBot data
This requires a few more arguments
Run python3 generator/generator.py --dataformat "CrowdBot" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
  • --input_tf with the path to the file with all tf frames.
  • --input_vel with the path to the file with all detection velocities.
Additionally the argument --label "False" can be added for both cases to only generate the input without generating all the label.
Scene segmentation
Train
  1. Create a text file at the location given by data_root in the config/config.yml file. This file should have all relative paths to the training data folder.
Optionally: The same can be done for the validation data using validation_dir as the config variable
  1. Run python3 engine/train.py to start training of the model.
Predict
Run python3 engine/predict.py with the following arguments.
  • --input with the location to the input images you want to predict
  • --output with the location where the predicted segmentations should be stored
  • --checkpoint with the path to the checkpoint file to restart at. If empty it will retrain with the given data.
About
Segmentation of crowded spaces, with conversion from 3D lidar detections to visual scene descriptors
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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Real-time Crowd Segmentation from 2D/3D lidar person detections
Simulating crowds of people in Isaac Sim
This repository...
If you find this code relevant for your work, please consider citing one or both of these papers. A bibtex entry is provided below:
@article{hughes2023foundations,
title={TODO},
author={TODO},
year={2024},
eprint={xxxx.xxxxx},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

Installation
Requirements
  • GPU for machine learning

    • When not using CUDA install pytorch manually!
Setup
  1. Create a virtual python environment with python3 -m venv env
  2. Enter into the environment using source env/bin/activate
  3. Install required packages pip install -r requirements.txt
Configuration
Most basic configurations for both input/label generation and the scene segmentation itself can be found in the config folder.
Run
Input and Label Generator
Requirements
Have detection data in either CrowdBot or JRDB (with additional velocities of all people) format. See this repository for a simulator to create data in JRDB format.
Usage with JRDB based data
Run python3 generator/generator.py --dataformat "JRDB" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
Usage with CrowdBot data
This requires a few more arguments
Run python3 generator/generator.py --dataformat "CrowdBot" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
  • --input_tf with the path to the file with all tf frames.
  • --input_vel with the path to the file with all detection velocities.
Additionally the argument --label "False" can be added for both cases to only generate the input without generating all the label.
Scene segmentation
Train
  1. Create a text file at the location given by data_root in the config/config.yml file. This file should have all relative paths to the training data folder.
Optionally: The same can be done for the validation data using validation_dir as the config variable
  1. Run python3 engine/train.py to start training of the model.
Predict
Run python3 engine/predict.py with the following arguments.
  • --input with the location to the input images you want to predict
  • --output with the location where the predicted segmentations should be stored
  • --checkpoint with the path to the checkpoint file to restart at. If empty it will retrain with the given data.
About
Segmentation of crowded spaces, with conversion from 3D lidar detections to visual scene descriptors
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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Real-time Crowd Segmentation from 2D/3D lidar person detections
Simulating crowds of people in Isaac Sim
This repository...
If you find this code relevant for your work, please consider citing one or both of these papers. A bibtex entry is provided below:
@article{hughes2023foundations,
title={TODO},
author={TODO},
year={2024},
eprint={xxxx.xxxxx},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

Installation
Requirements
  • GPU for machine learning

    • When not using CUDA install pytorch manually!
Setup
  1. Create a virtual python environment with python3 -m venv env
  2. Enter into the environment using source env/bin/activate
  3. Install required packages pip install -r requirements.txt
Configuration
Most basic configurations for both input/label generation and the scene segmentation itself can be found in the config folder.
Run
Input and Label Generator
Requirements
Have detection data in either CrowdBot or JRDB (with additional velocities of all people) format. See this repository for a simulator to create data in JRDB format.
Usage with JRDB based data
Run python3 generator/generator.py --dataformat "JRDB" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
Usage with CrowdBot data
This requires a few more arguments
Run python3 generator/generator.py --dataformat "CrowdBot" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
  • --input_tf with the path to the file with all tf frames.
  • --input_vel with the path to the file with all detection velocities.
Additionally the argument --label "False" can be added for both cases to only generate the input without generating all the label.
Scene segmentation
Train
  1. Create a text file at the location given by data_root in the config/config.yml file. This file should have all relative paths to the training data folder.
Optionally: The same can be done for the validation data using validation_dir as the config variable
  1. Run python3 engine/train.py to start training of the model.
Predict
Run python3 engine/predict.py with the following arguments.
  • --input with the location to the input images you want to predict
  • --output with the location where the predicted segmentations should be stored
  • --checkpoint with the path to the checkpoint file to restart at. If empty it will retrain with the given data.
About
Segmentation of crowded spaces, with conversion from 3D lidar detections to visual scene descriptors
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
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Real-time Crowd Segmentation from 2D/3D lidar person detections
Simulating crowds of people in Isaac Sim
This repository...
If you find this code relevant for your work, please consider citing one or both of these papers. A bibtex entry is provided below:
@article{hughes2023foundations,
title={TODO},
author={TODO},
year={2024},
eprint={xxxx.xxxxx},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

Installation
Requirements
  • GPU for machine learning

    • When not using CUDA install pytorch manually!
Setup
  1. Create a virtual python environment with python3 -m venv env
  2. Enter into the environment using source env/bin/activate
  3. Install required packages pip install -r requirements.txt
Configuration
Most basic configurations for both input/label generation and the scene segmentation itself can be found in the config folder.
Run
Input and Label Generator
Requirements
Have detection data in either CrowdBot or JRDB (with additional velocities of all people) format. See this repository for a simulator to create data in JRDB format.
Usage with JRDB based data
Run python3 generator/generator.py --dataformat "JRDB" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
Usage with CrowdBot data
This requires a few more arguments
Run python3 generator/generator.py --dataformat "CrowdBot" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
  • --input_tf with the path to the file with all tf frames.
  • --input_vel with the path to the file with all detection velocities.
Additionally the argument --label "False" can be added for both cases to only generate the input without generating all the label.
Scene segmentation
Train
  1. Create a text file at the location given by data_root in the config/config.yml file. This file should have all relative paths to the training data folder.
Optionally: The same can be done for the validation data using validation_dir as the config variable
  1. Run python3 engine/train.py to start training of the model.
Predict
Run python3 engine/predict.py with the following arguments.
  • --input with the location to the input images you want to predict
  • --output with the location where the predicted segmentations should be stored
  • --checkpoint with the path to the checkpoint file to restart at. If empty it will retrain with the given data.
About
Segmentation of crowded spaces, with conversion from 3D lidar detections to visual scene descriptors
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
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Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Real-time Crowd Segmentation from 2D/3D lidar person detections
Simulating crowds of people in Isaac Sim
This repository...
If you find this code relevant for your work, please consider citing one or both of these papers. A bibtex entry is provided below:
@article{hughes2023foundations,
title={TODO},
author={TODO},
year={2024},
eprint={xxxx.xxxxx},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

Installation
Requirements
  • GPU for machine learning

    • When not using CUDA install pytorch manually!
Setup
  1. Create a virtual python environment with python3 -m venv env
  2. Enter into the environment using source env/bin/activate
  3. Install required packages pip install -r requirements.txt
Configuration
Most basic configurations for both input/label generation and the scene segmentation itself can be found in the config folder.
Run
Input and Label Generator
Requirements
Have detection data in either CrowdBot or JRDB (with additional velocities of all people) format. See this repository for a simulator to create data in JRDB format.
Usage with JRDB based data
Run python3 generator/generator.py --dataformat "JRDB" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
Usage with CrowdBot data
This requires a few more arguments
Run python3 generator/generator.py --dataformat "CrowdBot" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
  • --input_tf with the path to the file with all tf frames.
  • --input_vel with the path to the file with all detection velocities.
Additionally the argument --label "False" can be added for both cases to only generate the input without generating all the label.
Scene segmentation
Train
  1. Create a text file at the location given by data_root in the config/config.yml file. This file should have all relative paths to the training data folder.
Optionally: The same can be done for the validation data using validation_dir as the config variable
  1. Run python3 engine/train.py to start training of the model.
Predict
Run python3 engine/predict.py with the following arguments.
  • --input with the location to the input images you want to predict
  • --output with the location where the predicted segmentations should be stored
  • --checkpoint with the path to the checkpoint file to restart at. If empty it will retrain with the given data.
About
Segmentation of crowded spaces, with conversion from 3D lidar detections to visual scene descriptors
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Real-time Crowd Segmentation from 2D/3D lidar person detections
Simulating crowds of people in Isaac Sim
This repository...
If you find this code relevant for your work, please consider citing one or both of these papers. A bibtex entry is provided below:
@article{hughes2023foundations,
title={TODO},
author={TODO},
year={2024},
eprint={xxxx.xxxxx},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

Installation
Requirements
  • GPU for machine learning

    • When not using CUDA install pytorch manually!
Setup
  1. Create a virtual python environment with python3 -m venv env
  2. Enter into the environment using source env/bin/activate
  3. Install required packages pip install -r requirements.txt
Configuration
Most basic configurations for both input/label generation and the scene segmentation itself can be found in the config folder.
Run
Input and Label Generator
Requirements
Have detection data in either CrowdBot or JRDB (with additional velocities of all people) format. See this repository for a simulator to create data in JRDB format.
Usage with JRDB based data
Run python3 generator/generator.py --dataformat "JRDB" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
Usage with CrowdBot data
This requires a few more arguments
Run python3 generator/generator.py --dataformat "CrowdBot" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
  • --input_tf with the path to the file with all tf frames.
  • --input_vel with the path to the file with all detection velocities.
Additionally the argument --label "False" can be added for both cases to only generate the input without generating all the label.
Scene segmentation
Train
  1. Create a text file at the location given by data_root in the config/config.yml file. This file should have all relative paths to the training data folder.
Optionally: The same can be done for the validation data using validation_dir as the config variable
  1. Run python3 engine/train.py to start training of the model.
Predict
Run python3 engine/predict.py with the following arguments.
  • --input with the location to the input images you want to predict
  • --output with the location where the predicted segmentations should be stored
  • --checkpoint with the path to the checkpoint file to restart at. If empty it will retrain with the given data.
About
Segmentation of crowded spaces, with conversion from 3D lidar detections to visual scene descriptors
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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Real-time Crowd Segmentation from 2D/3D lidar person detections
Simulating crowds of people in Isaac Sim
This repository...
If you find this code relevant for your work, please consider citing one or both of these papers. A bibtex entry is provided below:
@article{hughes2023foundations,
title={TODO},
author={TODO},
year={2024},
eprint={xxxx.xxxxx},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

Installation
Requirements
  • GPU for machine learning

    • When not using CUDA install pytorch manually!
Setup
  1. Create a virtual python environment with python3 -m venv env
  2. Enter into the environment using source env/bin/activate
  3. Install required packages pip install -r requirements.txt
Configuration
Most basic configurations for both input/label generation and the scene segmentation itself can be found in the config folder.
Run
Input and Label Generator
Requirements
Have detection data in either CrowdBot or JRDB (with additional velocities of all people) format. See this repository for a simulator to create data in JRDB format.
Usage with JRDB based data
Run python3 generator/generator.py --dataformat "JRDB" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
Usage with CrowdBot data
This requires a few more arguments
Run python3 generator/generator.py --dataformat "CrowdBot" with the following additional arguments.
  • --input with the path to your detection data
  • --output with the path where the generated images should be stored.
  • --input_tf with the path to the file with all tf frames.
  • --input_vel with the path to the file with all detection velocities.
Additionally the argument --label "False" can be added for both cases to only generate the input without generating all the label.
Scene segmentation
Train
  1. Create a text file at the location given by data_root in the config/config.yml file. This file should have all relative paths to the training data folder.
Optionally: The same can be done for the validation data using validation_dir as the config variable
  1. Run python3 engine/train.py to start training of the model.
Predict
Run python3 engine/predict.py with the following arguments.
  • --input with the location to the input images you want to predict
  • --output with the location where the predicted segmentations should be stored
  • --checkpoint with the path to the checkpoint file to restart at. If empty it will retrain with the given data.
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Segmentation of crowded spaces, with conversion from 3D lidar detections to visual scene descriptors
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GPL-3.0 license
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