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HAAR_DeepModel (v.0.5)

Open Source Algorithm For Detecting Ship Passengers’ Abnormal Behaviors And Fall Accidents

Preventing accident on ship and alert for help is the goal for the detection algorithm. Prevent more accidents, save more lives.
Explore the docs »

View Demo · Report Bug · Request Feature

animated

Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Acknowledgments

About The Project

Open Source CCTV based AI algorithm which detects the abnormal behaviors of passengers on the ship to predict the possible accidents and warn the on board sailors. When the CCTV catches the actual accidents, the algorithm will alert the incidents and the current accident location to nearby coast guards in real time in order to increase the rescue rate for the fallen passengers.

We defined activity of ship-passenger.
[Walking, Move-Over, Standing ... ]

(back to top)

Requirements

  • Cuda, Cudnn : Cuda support GPU Device (We implemented RTX 3090)
  • Detectron 2
    • Linux or macOS with Python ≥ 3.7
    • PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
    • OpenCV is optional but needed by demo and visualization
    • See Detectron Install.md
  • AdelaiDet

Download pretrain Model

Download pretrain Model : FCOS

Download pretrain Model : Boxinst

(back to top)

Getting Started

The model can be started by executing haar_demo.py in /HAAR_Demo directory.

[Sample Run Script]

python HAAR_Demo/haar_demo.py \
--video-input ./HAAR_Demo/cctv_demo.mp4 \
--opts MODEL.WEIGHTS ./models/fcpose50.pth

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

(back to top)

License

Distributed under the MIT License. See LICENSE.txt for more information.

(back to top)

Acknowledgments

(back to top)

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

0 stars

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0 watching

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0 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
ContributorsForksStargazersIssuesMIT LicenseLinkedIn

 HAAR_DeepModel (v.0.5) 
Open Source Algorithm For Detecting Ship Passengers’ Abnormal Behaviors And Fall Accidents

Preventing accident on ship and alert for help is the goal for the detection algorithm. Prevent more accidents, save more lives. 
Explore the docs »

View Demo
·
Report Bug
·
Request Feature
animated
Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Acknowledgments
About The Project
Open Source CCTV based AI algorithm which detects the abnormal behaviors of passengers on the ship to predict the possible accidents and warn the on board sailors. When the CCTV catches the actual accidents, the algorithm will alert the incidents and the current accident location to nearby coast guards in real time in order to increase the rescue rate for the fallen passengers.
We defined activity of ship-passenger.
[Walking, Move-Over, Standing ... ]
(back to top)
Requirements
  • Cuda, Cudnn : Cuda support GPU Device (We implemented RTX 3090)
  • Detectron 2

    • Linux or macOS with Python ≥ 3.7
    • PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
    • OpenCV is optional but needed by demo and visualization
    • See Detectron Install.md
  • AdelaiDet

Download pretrain Model
Download pretrain Model : FCOS
Download pretrain Model : Boxinst
(back to top)
Getting Started
The model can be started by executing haar_demo.py in /HAAR_Demo directory.
[Sample Run Script]
python HAAR_Demo/haar_demo.py \
--video-input ./HAAR_Demo/cctv_demo.mp4 \
--opts MODEL.WEIGHTS ./models/fcpose50.pth

Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request
(back to top)
License
Distributed under the MIT License. See LICENSE.txt for more information.
(back to top)
Acknowledgments
(back to top)
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
Report repository
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, '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");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
Repository files navigation
ContributorsForksStargazersIssuesMIT LicenseLinkedIn

 HAAR_DeepModel (v.0.5) 
Open Source Algorithm For Detecting Ship Passengers’ Abnormal Behaviors And Fall Accidents

Preventing accident on ship and alert for help is the goal for the detection algorithm. Prevent more accidents, save more lives. 
Explore the docs »

View Demo
·
Report Bug
·
Request Feature
animated
Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Acknowledgments
About The Project
Open Source CCTV based AI algorithm which detects the abnormal behaviors of passengers on the ship to predict the possible accidents and warn the on board sailors. When the CCTV catches the actual accidents, the algorithm will alert the incidents and the current accident location to nearby coast guards in real time in order to increase the rescue rate for the fallen passengers.
We defined activity of ship-passenger.
[Walking, Move-Over, Standing ... ]
(back to top)
Requirements
  • Cuda, Cudnn : Cuda support GPU Device (We implemented RTX 3090)
  • Detectron 2

    • Linux or macOS with Python ≥ 3.7
    • PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
    • OpenCV is optional but needed by demo and visualization
    • See Detectron Install.md
  • AdelaiDet

Download pretrain Model
Download pretrain Model : FCOS
Download pretrain Model : Boxinst
(back to top)
Getting Started
The model can be started by executing haar_demo.py in /HAAR_Demo directory.
[Sample Run Script]
python HAAR_Demo/haar_demo.py \
--video-input ./HAAR_Demo/cctv_demo.mp4 \
--opts MODEL.WEIGHTS ./models/fcpose50.pth

Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request
(back to top)
License
Distributed under the MIT License. See LICENSE.txt for more information.
(back to top)
Acknowledgments
(back to top)
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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('^' + ".*" + '

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README.md
Repository files navigation
ContributorsForksStargazersIssuesMIT LicenseLinkedIn

 HAAR_DeepModel (v.0.5) 
Open Source Algorithm For Detecting Ship Passengers’ Abnormal Behaviors And Fall Accidents

Preventing accident on ship and alert for help is the goal for the detection algorithm. Prevent more accidents, save more lives. 
Explore the docs »

View Demo
·
Report Bug
·
Request Feature
animated
Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Acknowledgments
About The Project
Open Source CCTV based AI algorithm which detects the abnormal behaviors of passengers on the ship to predict the possible accidents and warn the on board sailors. When the CCTV catches the actual accidents, the algorithm will alert the incidents and the current accident location to nearby coast guards in real time in order to increase the rescue rate for the fallen passengers.
We defined activity of ship-passenger.
[Walking, Move-Over, Standing ... ]
(back to top)
Requirements
  • Cuda, Cudnn : Cuda support GPU Device (We implemented RTX 3090)
  • Detectron 2

    • Linux or macOS with Python ≥ 3.7
    • PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
    • OpenCV is optional but needed by demo and visualization
    • See Detectron Install.md
  • AdelaiDet

Download pretrain Model
Download pretrain Model : FCOS
Download pretrain Model : Boxinst
(back to top)
Getting Started
The model can be started by executing haar_demo.py in /HAAR_Demo directory.
[Sample Run Script]
python HAAR_Demo/haar_demo.py \
--video-input ./HAAR_Demo/cctv_demo.mp4 \
--opts MODEL.WEIGHTS ./models/fcpose50.pth

Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request
(back to top)
License
Distributed under the MIT License. See LICENSE.txt for more information.
(back to top)
Acknowledgments
(back to top)
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, '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" + '

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detectron2
detectron2
README.md
README.md
Repository files navigation
ContributorsForksStargazersIssuesMIT LicenseLinkedIn

 HAAR_DeepModel (v.0.5) 
Open Source Algorithm For Detecting Ship Passengers’ Abnormal Behaviors And Fall Accidents

Preventing accident on ship and alert for help is the goal for the detection algorithm. Prevent more accidents, save more lives. 
Explore the docs »

View Demo
·
Report Bug
·
Request Feature
animated
Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Acknowledgments
About The Project
Open Source CCTV based AI algorithm which detects the abnormal behaviors of passengers on the ship to predict the possible accidents and warn the on board sailors. When the CCTV catches the actual accidents, the algorithm will alert the incidents and the current accident location to nearby coast guards in real time in order to increase the rescue rate for the fallen passengers.
We defined activity of ship-passenger.
[Walking, Move-Over, Standing ... ]
(back to top)
Requirements
  • Cuda, Cudnn : Cuda support GPU Device (We implemented RTX 3090)
  • Detectron 2

    • Linux or macOS with Python ≥ 3.7
    • PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
    • OpenCV is optional but needed by demo and visualization
    • See Detectron Install.md
  • AdelaiDet

Download pretrain Model
Download pretrain Model : FCOS
Download pretrain Model : Boxinst
(back to top)
Getting Started
The model can be started by executing haar_demo.py in /HAAR_Demo directory.
[Sample Run Script]
python HAAR_Demo/haar_demo.py \
--video-input ./HAAR_Demo/cctv_demo.mp4 \
--opts MODEL.WEIGHTS ./models/fcpose50.pth

Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request
(back to top)
License
Distributed under the MIT License. See LICENSE.txt for more information.
(back to top)
Acknowledgments
(back to top)
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
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Latest commit
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23 Commits
Folders and files
NameName
Last commit message
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.idea
.idea
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AdelaiDet
HAAR_Demo
HAAR_Demo
detectron2
detectron2
README.md
README.md
Repository files navigation
ContributorsForksStargazersIssuesMIT LicenseLinkedIn

 HAAR_DeepModel (v.0.5) 
Open Source Algorithm For Detecting Ship Passengers’ Abnormal Behaviors And Fall Accidents

Preventing accident on ship and alert for help is the goal for the detection algorithm. Prevent more accidents, save more lives. 
Explore the docs »

View Demo
·
Report Bug
·
Request Feature
animated
Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Acknowledgments
About The Project
Open Source CCTV based AI algorithm which detects the abnormal behaviors of passengers on the ship to predict the possible accidents and warn the on board sailors. When the CCTV catches the actual accidents, the algorithm will alert the incidents and the current accident location to nearby coast guards in real time in order to increase the rescue rate for the fallen passengers.
We defined activity of ship-passenger.
[Walking, Move-Over, Standing ... ]
(back to top)
Requirements
  • Cuda, Cudnn : Cuda support GPU Device (We implemented RTX 3090)
  • Detectron 2

    • Linux or macOS with Python ≥ 3.7
    • PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
    • OpenCV is optional but needed by demo and visualization
    • See Detectron Install.md
  • AdelaiDet

Download pretrain Model
Download pretrain Model : FCOS
Download pretrain Model : Boxinst
(back to top)
Getting Started
The model can be started by executing haar_demo.py in /HAAR_Demo directory.
[Sample Run Script]
python HAAR_Demo/haar_demo.py \
--video-input ./HAAR_Demo/cctv_demo.mp4 \
--opts MODEL.WEIGHTS ./models/fcpose50.pth

Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request
(back to top)
License
Distributed under the MIT License. See LICENSE.txt for more information.
(back to top)
Acknowledgments
(back to top)
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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('^' + ".*" + '

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23 Commits
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AdelaiDet
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HAAR_Demo
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detectron2
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README.md
Repository files navigation
ContributorsForksStargazersIssuesMIT LicenseLinkedIn

 HAAR_DeepModel (v.0.5) 
Open Source Algorithm For Detecting Ship Passengers’ Abnormal Behaviors And Fall Accidents

Preventing accident on ship and alert for help is the goal for the detection algorithm. Prevent more accidents, save more lives. 
Explore the docs »

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Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Acknowledgments
About The Project
Open Source CCTV based AI algorithm which detects the abnormal behaviors of passengers on the ship to predict the possible accidents and warn the on board sailors. When the CCTV catches the actual accidents, the algorithm will alert the incidents and the current accident location to nearby coast guards in real time in order to increase the rescue rate for the fallen passengers.
We defined activity of ship-passenger.
[Walking, Move-Over, Standing ... ]
(back to top)
Requirements
  • Cuda, Cudnn : Cuda support GPU Device (We implemented RTX 3090)
  • Detectron 2

    • Linux or macOS with Python ≥ 3.7
    • PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
    • OpenCV is optional but needed by demo and visualization
    • See Detectron Install.md
  • AdelaiDet

Download pretrain Model
Download pretrain Model : FCOS
Download pretrain Model : Boxinst
(back to top)
Getting Started
The model can be started by executing haar_demo.py in /HAAR_Demo directory.
[Sample Run Script]
python HAAR_Demo/haar_demo.py \
--video-input ./HAAR_Demo/cctv_demo.mp4 \
--opts MODEL.WEIGHTS ./models/fcpose50.pth

Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request
(back to top)
License
Distributed under the MIT License. See LICENSE.txt for more information.
(back to top)
Acknowledgments
(back to top)
About
No description, website, or topics provided.
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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 HAAR_DeepModel (v.0.5) 
Open Source Algorithm For Detecting Ship Passengers’ Abnormal Behaviors And Fall Accidents

Preventing accident on ship and alert for help is the goal for the detection algorithm. Prevent more accidents, save more lives. 
Explore the docs »

View Demo
·
Report Bug
·
Request Feature
animated
Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Acknowledgments
About The Project
Open Source CCTV based AI algorithm which detects the abnormal behaviors of passengers on the ship to predict the possible accidents and warn the on board sailors. When the CCTV catches the actual accidents, the algorithm will alert the incidents and the current accident location to nearby coast guards in real time in order to increase the rescue rate for the fallen passengers.
We defined activity of ship-passenger.
[Walking, Move-Over, Standing ... ]
(back to top)
Requirements
  • Cuda, Cudnn : Cuda support GPU Device (We implemented RTX 3090)
  • Detectron 2

    • Linux or macOS with Python ≥ 3.7
    • PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
    • OpenCV is optional but needed by demo and visualization
    • See Detectron Install.md
  • AdelaiDet

Download pretrain Model
Download pretrain Model : FCOS
Download pretrain Model : Boxinst
(back to top)
Getting Started
The model can be started by executing haar_demo.py in /HAAR_Demo directory.
[Sample Run Script]
python HAAR_Demo/haar_demo.py \
--video-input ./HAAR_Demo/cctv_demo.mp4 \
--opts MODEL.WEIGHTS ./models/fcpose50.pth

Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!
  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request
(back to top)
License
Distributed under the MIT License. See LICENSE.txt for more information.
(back to top)
Acknowledgments
(back to top)
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages