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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.