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mcf

Introduction

This library provides data structures and solvers for multi-frame data association, suitable for the min-cost flow formulation of multiple object tracking [1]. The library provides:

  • Solvers for the multiple object tracking problem based on COIN-OR Clp and COIN-OR Lemon,
  • an implementation of an efficient k-shortest path solver [2],
  • a convenience class to optimize the sequence in batches over a fixed-length optimization window,
  • Python bindings.

Dependencies

Software development is carried out in Linux. The library should compile and run on other systems with few changes to the build system, but this has not been tested. The core library has no dependencies other than a recent version of CMake (>= 3.2.0) and a C++14 compliant compiler. However, depending on the specific feature set, optional dependencies may be required:

The library ships with a Makefile to install these dependencies locally.

Installation

First, clone the repository and create a build directory:

git clone https://github.com/nwojke/mcf.git
mkdir mcf/build
cd mcf/build

Then, install dependencies using the provided Makefile (these commands must be called from within the build directory):

# The following command installs pybind11 to enable Python bindings. This
# should usually be sufficient.
make -f ../Makefile.external pybind11
# Alternatively, you may choose to install all dependencies, but this may take
# a while.
make -f ../Makefile.external

Then, configure the project and build the library (again, we are inside the build directory):

# -DMCF_BUILD_PYTHON=ON to explicitly enable Python binds
# -DMCF_BUILD_STATIC=ON to compile a static library
cmake -DCMAKE_BUILD_TYPE=RELEASE -DMCF_BUILD_PYTHON=ON -DMCF_BUILD_STATIC=ON ..
make

On completion, the static library should reside in build/lib, the compiled Python module in build/python_lib, and library header files in build/include. Installation scripts are currently not provided; simply copy the files over to your development environment or modify the include and library paths accordingly.

Getting started

The main data structure is the tracking graph defined in graph.hpp. An efficient k-shortest path solver is defined in k_shortest_path_solver.hpp. The batch solver is defined in batch_processing.hpp. All of the classes have accompanying documentation that should help you get started. For a minimalistic example check out the examples directory.

Example application

A complete implementation of a tracking method using this library can be found in a seperate project.

Literature

  1. Zhang, Li, Nevatia: Global data association for multi-object tracking using network flows, CVPR (2008).
  2. Berclaz, Fleuret, Tueretken, Fua: Multiple Object Tracking using K-shortes Paths, PAMI, 33(9), 2011.

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C++/Python library for multiple object tracking via min-cost flow algorithms

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, '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
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mcf
Introduction
This library provides data structures and solvers for multi-frame data
association, suitable for the min-cost flow formulation of multiple object
tracking [1]. The library provides:
  • Solvers for the multiple object tracking problem based on COIN-OR Clp and
COIN-OR Lemon,
  • an implementation of an efficient k-shortest path solver [2],
  • a convenience class to optimize the sequence in batches over a fixed-length
optimization window,
  • Python bindings.
Dependencies
Software development is carried out in Linux. The library should compile and
run on other systems with few changes to the build system, but this has
not been tested. The core library has no dependencies other than a recent
version of CMake (>= 3.2.0) and a C++14 compliant compiler. However, depending
on the specific feature set, optional dependencies may be required:
The library ships with a Makefile to install these dependencies locally.
Installation
First, clone the repository and create a build directory:
git clone https://github.com/nwojke/mcf.git
mkdir mcf/build
cd mcf/build

Then, install dependencies using the provided Makefile (these commands must be
called from within the build directory):
# The following command installs pybind11 to enable Python bindings. This
# should usually be sufficient.
make -f ../Makefile.external pybind11
# Alternatively, you may choose to install all dependencies, but this may take
# a while.
make -f ../Makefile.external

Then, configure the project and build the library (again, we are inside the
build directory):
# -DMCF_BUILD_PYTHON=ON to explicitly enable Python binds
# -DMCF_BUILD_STATIC=ON to compile a static library
cmake -DCMAKE_BUILD_TYPE=RELEASE -DMCF_BUILD_PYTHON=ON -DMCF_BUILD_STATIC=ON ..
make

On completion, the static library should reside in build/lib, the compiled
Python module in build/python_lib, and library header files in
build/include. Installation scripts are currently not provided; simply
copy the files over to your development environment or modify the include
and library paths accordingly.
Getting started
The main data structure is the tracking graph defined in graph.hpp.
An efficient k-shortest path solver is defined in k_shortest_path_solver.hpp.
The batch solver is defined in batch_processing.hpp.
All of the classes have accompanying documentation that should help you get
started. For a minimalistic example check out the examples directory.
Example application
A complete implementation of a tracking method using this library can be found
in a seperate project.
Literature
  1. Zhang, Li, Nevatia: Global data association for multi-object tracking
using network flows, CVPR (2008).
  2. Berclaz, Fleuret, Tueretken, Fua: Multiple Object Tracking using
K-shortes Paths, PAMI, 33(9), 2011.
About
C++/Python library for multiple object tracking via min-cost flow algorithms
Resources
Readme
GPL-3.0 license
Activity
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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('^' + ".*" + '

Skip to content
BranchesTags
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mcf
Introduction
This library provides data structures and solvers for multi-frame data
association, suitable for the min-cost flow formulation of multiple object
tracking [1]. The library provides:
  • Solvers for the multiple object tracking problem based on COIN-OR Clp and
COIN-OR Lemon,
  • an implementation of an efficient k-shortest path solver [2],
  • a convenience class to optimize the sequence in batches over a fixed-length
optimization window,
  • Python bindings.
Dependencies
Software development is carried out in Linux. The library should compile and
run on other systems with few changes to the build system, but this has
not been tested. The core library has no dependencies other than a recent
version of CMake (>= 3.2.0) and a C++14 compliant compiler. However, depending
on the specific feature set, optional dependencies may be required:
The library ships with a Makefile to install these dependencies locally.
Installation
First, clone the repository and create a build directory:
git clone https://github.com/nwojke/mcf.git
mkdir mcf/build
cd mcf/build

Then, install dependencies using the provided Makefile (these commands must be
called from within the build directory):
# The following command installs pybind11 to enable Python bindings. This
# should usually be sufficient.
make -f ../Makefile.external pybind11
# Alternatively, you may choose to install all dependencies, but this may take
# a while.
make -f ../Makefile.external

Then, configure the project and build the library (again, we are inside the
build directory):
# -DMCF_BUILD_PYTHON=ON to explicitly enable Python binds
# -DMCF_BUILD_STATIC=ON to compile a static library
cmake -DCMAKE_BUILD_TYPE=RELEASE -DMCF_BUILD_PYTHON=ON -DMCF_BUILD_STATIC=ON ..
make

On completion, the static library should reside in build/lib, the compiled
Python module in build/python_lib, and library header files in
build/include. Installation scripts are currently not provided; simply
copy the files over to your development environment or modify the include
and library paths accordingly.
Getting started
The main data structure is the tracking graph defined in graph.hpp.
An efficient k-shortest path solver is defined in k_shortest_path_solver.hpp.
The batch solver is defined in batch_processing.hpp.
All of the classes have accompanying documentation that should help you get
started. For a minimalistic example check out the examples directory.
Example application
A complete implementation of a tracking method using this library can be found
in a seperate project.
Literature
  1. Zhang, Li, Nevatia: Global data association for multi-object tracking
using network flows, CVPR (2008).
  2. Berclaz, Fleuret, Tueretken, Fua: Multiple Object Tracking using
K-shortes Paths, PAMI, 33(9), 2011.
About
C++/Python library for multiple object tracking via min-cost flow algorithms
Resources
Readme
GPL-3.0 license
Activity
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0 forks
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, '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
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mcf
Introduction
This library provides data structures and solvers for multi-frame data
association, suitable for the min-cost flow formulation of multiple object
tracking [1]. The library provides:
  • Solvers for the multiple object tracking problem based on COIN-OR Clp and
COIN-OR Lemon,
  • an implementation of an efficient k-shortest path solver [2],
  • a convenience class to optimize the sequence in batches over a fixed-length
optimization window,
  • Python bindings.
Dependencies
Software development is carried out in Linux. The library should compile and
run on other systems with few changes to the build system, but this has
not been tested. The core library has no dependencies other than a recent
version of CMake (>= 3.2.0) and a C++14 compliant compiler. However, depending
on the specific feature set, optional dependencies may be required:
The library ships with a Makefile to install these dependencies locally.
Installation
First, clone the repository and create a build directory:
git clone https://github.com/nwojke/mcf.git
mkdir mcf/build
cd mcf/build

Then, install dependencies using the provided Makefile (these commands must be
called from within the build directory):
# The following command installs pybind11 to enable Python bindings. This
# should usually be sufficient.
make -f ../Makefile.external pybind11
# Alternatively, you may choose to install all dependencies, but this may take
# a while.
make -f ../Makefile.external

Then, configure the project and build the library (again, we are inside the
build directory):
# -DMCF_BUILD_PYTHON=ON to explicitly enable Python binds
# -DMCF_BUILD_STATIC=ON to compile a static library
cmake -DCMAKE_BUILD_TYPE=RELEASE -DMCF_BUILD_PYTHON=ON -DMCF_BUILD_STATIC=ON ..
make

On completion, the static library should reside in build/lib, the compiled
Python module in build/python_lib, and library header files in
build/include. Installation scripts are currently not provided; simply
copy the files over to your development environment or modify the include
and library paths accordingly.
Getting started
The main data structure is the tracking graph defined in graph.hpp.
An efficient k-shortest path solver is defined in k_shortest_path_solver.hpp.
The batch solver is defined in batch_processing.hpp.
All of the classes have accompanying documentation that should help you get
started. For a minimalistic example check out the examples directory.
Example application
A complete implementation of a tracking method using this library can be found
in a seperate project.
Literature
  1. Zhang, Li, Nevatia: Global data association for multi-object tracking
using network flows, CVPR (2008).
  2. Berclaz, Fleuret, Tueretken, Fua: Multiple Object Tracking using
K-shortes Paths, PAMI, 33(9), 2011.
About
C++/Python library for multiple object tracking via min-cost flow algorithms
Resources
Readme
GPL-3.0 license
Activity
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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" + '

Skip to content
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mcf
Introduction
This library provides data structures and solvers for multi-frame data
association, suitable for the min-cost flow formulation of multiple object
tracking [1]. The library provides:
  • Solvers for the multiple object tracking problem based on COIN-OR Clp and
COIN-OR Lemon,
  • an implementation of an efficient k-shortest path solver [2],
  • a convenience class to optimize the sequence in batches over a fixed-length
optimization window,
  • Python bindings.
Dependencies
Software development is carried out in Linux. The library should compile and
run on other systems with few changes to the build system, but this has
not been tested. The core library has no dependencies other than a recent
version of CMake (>= 3.2.0) and a C++14 compliant compiler. However, depending
on the specific feature set, optional dependencies may be required:
The library ships with a Makefile to install these dependencies locally.
Installation
First, clone the repository and create a build directory:
git clone https://github.com/nwojke/mcf.git
mkdir mcf/build
cd mcf/build

Then, install dependencies using the provided Makefile (these commands must be
called from within the build directory):
# The following command installs pybind11 to enable Python bindings. This
# should usually be sufficient.
make -f ../Makefile.external pybind11
# Alternatively, you may choose to install all dependencies, but this may take
# a while.
make -f ../Makefile.external

Then, configure the project and build the library (again, we are inside the
build directory):
# -DMCF_BUILD_PYTHON=ON to explicitly enable Python binds
# -DMCF_BUILD_STATIC=ON to compile a static library
cmake -DCMAKE_BUILD_TYPE=RELEASE -DMCF_BUILD_PYTHON=ON -DMCF_BUILD_STATIC=ON ..
make

On completion, the static library should reside in build/lib, the compiled
Python module in build/python_lib, and library header files in
build/include. Installation scripts are currently not provided; simply
copy the files over to your development environment or modify the include
and library paths accordingly.
Getting started
The main data structure is the tracking graph defined in graph.hpp.
An efficient k-shortest path solver is defined in k_shortest_path_solver.hpp.
The batch solver is defined in batch_processing.hpp.
All of the classes have accompanying documentation that should help you get
started. For a minimalistic example check out the examples directory.
Example application
A complete implementation of a tracking method using this library can be found
in a seperate project.
Literature
  1. Zhang, Li, Nevatia: Global data association for multi-object tracking
using network flows, CVPR (2008).
  2. Berclaz, Fleuret, Tueretken, Fua: Multiple Object Tracking using
K-shortes Paths, PAMI, 33(9), 2011.
About
C++/Python library for multiple object tracking via min-cost flow algorithms
Resources
Readme
GPL-3.0 license
Activity
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0 forks
Report repository
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, '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
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mcf
Introduction
This library provides data structures and solvers for multi-frame data
association, suitable for the min-cost flow formulation of multiple object
tracking [1]. The library provides:
  • Solvers for the multiple object tracking problem based on COIN-OR Clp and
COIN-OR Lemon,
  • an implementation of an efficient k-shortest path solver [2],
  • a convenience class to optimize the sequence in batches over a fixed-length
optimization window,
  • Python bindings.
Dependencies
Software development is carried out in Linux. The library should compile and
run on other systems with few changes to the build system, but this has
not been tested. The core library has no dependencies other than a recent
version of CMake (>= 3.2.0) and a C++14 compliant compiler. However, depending
on the specific feature set, optional dependencies may be required:
The library ships with a Makefile to install these dependencies locally.
Installation
First, clone the repository and create a build directory:
git clone https://github.com/nwojke/mcf.git
mkdir mcf/build
cd mcf/build

Then, install dependencies using the provided Makefile (these commands must be
called from within the build directory):
# The following command installs pybind11 to enable Python bindings. This
# should usually be sufficient.
make -f ../Makefile.external pybind11
# Alternatively, you may choose to install all dependencies, but this may take
# a while.
make -f ../Makefile.external

Then, configure the project and build the library (again, we are inside the
build directory):
# -DMCF_BUILD_PYTHON=ON to explicitly enable Python binds
# -DMCF_BUILD_STATIC=ON to compile a static library
cmake -DCMAKE_BUILD_TYPE=RELEASE -DMCF_BUILD_PYTHON=ON -DMCF_BUILD_STATIC=ON ..
make

On completion, the static library should reside in build/lib, the compiled
Python module in build/python_lib, and library header files in
build/include. Installation scripts are currently not provided; simply
copy the files over to your development environment or modify the include
and library paths accordingly.
Getting started
The main data structure is the tracking graph defined in graph.hpp.
An efficient k-shortest path solver is defined in k_shortest_path_solver.hpp.
The batch solver is defined in batch_processing.hpp.
All of the classes have accompanying documentation that should help you get
started. For a minimalistic example check out the examples directory.
Example application
A complete implementation of a tracking method using this library can be found
in a seperate project.
Literature
  1. Zhang, Li, Nevatia: Global data association for multi-object tracking
using network flows, CVPR (2008).
  2. Berclaz, Fleuret, Tueretken, Fua: Multiple Object Tracking using
K-shortes Paths, PAMI, 33(9), 2011.
About
C++/Python library for multiple object tracking via min-cost flow algorithms
Resources
Readme
GPL-3.0 license
Activity
Custom properties
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0 forks
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, '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
mcf
Introduction
This library provides data structures and solvers for multi-frame data
association, suitable for the min-cost flow formulation of multiple object
tracking [1]. The library provides:
  • Solvers for the multiple object tracking problem based on COIN-OR Clp and
COIN-OR Lemon,
  • an implementation of an efficient k-shortest path solver [2],
  • a convenience class to optimize the sequence in batches over a fixed-length
optimization window,
  • Python bindings.
Dependencies
Software development is carried out in Linux. The library should compile and
run on other systems with few changes to the build system, but this has
not been tested. The core library has no dependencies other than a recent
version of CMake (>= 3.2.0) and a C++14 compliant compiler. However, depending
on the specific feature set, optional dependencies may be required:
The library ships with a Makefile to install these dependencies locally.
Installation
First, clone the repository and create a build directory:
git clone https://github.com/nwojke/mcf.git
mkdir mcf/build
cd mcf/build

Then, install dependencies using the provided Makefile (these commands must be
called from within the build directory):
# The following command installs pybind11 to enable Python bindings. This
# should usually be sufficient.
make -f ../Makefile.external pybind11
# Alternatively, you may choose to install all dependencies, but this may take
# a while.
make -f ../Makefile.external

Then, configure the project and build the library (again, we are inside the
build directory):
# -DMCF_BUILD_PYTHON=ON to explicitly enable Python binds
# -DMCF_BUILD_STATIC=ON to compile a static library
cmake -DCMAKE_BUILD_TYPE=RELEASE -DMCF_BUILD_PYTHON=ON -DMCF_BUILD_STATIC=ON ..
make

On completion, the static library should reside in build/lib, the compiled
Python module in build/python_lib, and library header files in
build/include. Installation scripts are currently not provided; simply
copy the files over to your development environment or modify the include
and library paths accordingly.
Getting started
The main data structure is the tracking graph defined in graph.hpp.
An efficient k-shortest path solver is defined in k_shortest_path_solver.hpp.
The batch solver is defined in batch_processing.hpp.
All of the classes have accompanying documentation that should help you get
started. For a minimalistic example check out the examples directory.
Example application
A complete implementation of a tracking method using this library can be found
in a seperate project.
Literature
  1. Zhang, Li, Nevatia: Global data association for multi-object tracking
using network flows, CVPR (2008).
  2. Berclaz, Fleuret, Tueretken, Fua: Multiple Object Tracking using
K-shortes Paths, PAMI, 33(9), 2011.
About
C++/Python library for multiple object tracking via min-cost flow algorithms
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Readme
GPL-3.0 license
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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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mcf
Introduction
This library provides data structures and solvers for multi-frame data
association, suitable for the min-cost flow formulation of multiple object
tracking [1]. The library provides:
  • Solvers for the multiple object tracking problem based on COIN-OR Clp and
COIN-OR Lemon,
  • an implementation of an efficient k-shortest path solver [2],
  • a convenience class to optimize the sequence in batches over a fixed-length
optimization window,
  • Python bindings.
Dependencies
Software development is carried out in Linux. The library should compile and
run on other systems with few changes to the build system, but this has
not been tested. The core library has no dependencies other than a recent
version of CMake (>= 3.2.0) and a C++14 compliant compiler. However, depending
on the specific feature set, optional dependencies may be required:
The library ships with a Makefile to install these dependencies locally.
Installation
First, clone the repository and create a build directory:
git clone https://github.com/nwojke/mcf.git
mkdir mcf/build
cd mcf/build

Then, install dependencies using the provided Makefile (these commands must be
called from within the build directory):
# The following command installs pybind11 to enable Python bindings. This
# should usually be sufficient.
make -f ../Makefile.external pybind11
# Alternatively, you may choose to install all dependencies, but this may take
# a while.
make -f ../Makefile.external

Then, configure the project and build the library (again, we are inside the
build directory):
# -DMCF_BUILD_PYTHON=ON to explicitly enable Python binds
# -DMCF_BUILD_STATIC=ON to compile a static library
cmake -DCMAKE_BUILD_TYPE=RELEASE -DMCF_BUILD_PYTHON=ON -DMCF_BUILD_STATIC=ON ..
make

On completion, the static library should reside in build/lib, the compiled
Python module in build/python_lib, and library header files in
build/include. Installation scripts are currently not provided; simply
copy the files over to your development environment or modify the include
and library paths accordingly.
Getting started
The main data structure is the tracking graph defined in graph.hpp.
An efficient k-shortest path solver is defined in k_shortest_path_solver.hpp.
The batch solver is defined in batch_processing.hpp.
All of the classes have accompanying documentation that should help you get
started. For a minimalistic example check out the examples directory.
Example application
A complete implementation of a tracking method using this library can be found
in a seperate project.
Literature
  1. Zhang, Li, Nevatia: Global data association for multi-object tracking
using network flows, CVPR (2008).
  2. Berclaz, Fleuret, Tueretken, Fua: Multiple Object Tracking using
K-shortes Paths, PAMI, 33(9), 2011.
About
C++/Python library for multiple object tracking via min-cost flow algorithms
Resources
Readme
GPL-3.0 license
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages