Repository: yasenh/sort-cpp Branch: master Commit: 1dcafbaa4351 Files: 29 Total size: 1.9 MB Directory structure: gitextract__xtzkjbt/ ├── .gitignore ├── CMakeLists.txt ├── Dockerfile ├── LICENSE ├── README.md ├── data/ │ ├── ADL-Rundle-6/ │ │ └── det.txt │ ├── ADL-Rundle-8/ │ │ └── det.txt │ ├── ETH-Bahnhof/ │ │ └── det.txt │ ├── ETH-Pedcross2/ │ │ └── det.txt │ ├── ETH-Sunnyday/ │ │ └── det.txt │ ├── KITTI-13/ │ │ └── det.txt │ ├── KITTI-17/ │ │ └── det.txt │ ├── PETS09-S2L1/ │ │ └── det.txt │ ├── TUD-Campus/ │ │ └── det.txt │ ├── TUD-Stadtmitte/ │ │ └── det.txt │ └── Venice-2/ │ └── det.txt ├── docker_run.sh ├── include/ │ ├── kalman_filter.h │ ├── matrix.cpp │ ├── matrix.h │ ├── munkres.h │ ├── track.h │ ├── tracker.h │ └── utils.h └── src/ ├── kalman_filter.cpp ├── main.cpp ├── munkres.cpp ├── track.cpp └── tracker.cpp ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ bin/ build/ .idea/ cmake-*/ mot_benchmark/ output/ ================================================ FILE: CMakeLists.txt ================================================ cmake_minimum_required(VERSION 3.5.1) project(sort-cpp) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall") set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/bin) # Try to find OpenCV find_package(OpenCV REQUIRED) if (OpenCV_FOUND) # If the package has been found, several variables will # be set, you can find the full list with descriptions # in the OpenCVConfig.cmake file. # Print some message showing some of them message(STATUS "OpenCV library status:") message(STATUS " version: ${OpenCV_VERSION}") message(STATUS " include path: ${OpenCV_INCLUDE_DIRS}") else () message(FATAL_ERROR "Could not locate OpenCV") endif() # Try to find Boost find_package(Boost COMPONENTS program_options filesystem REQUIRED) if(Boost_FOUND) include_directories(${Boost_INCLUDE_DIRS}) else () message(FATAL_ERROR "Could not locate Boost") endif() include_directories(${PROJECT_SOURCE_DIR}/include) file(GLOB SOURCE_FILES src/*.cpp) add_executable(${CMAKE_PROJECT_NAME} ${SOURCE_FILES}) target_link_libraries ( ${CMAKE_PROJECT_NAME} ${OpenCV_LIBS} ${Boost_LIBRARIES} ) ================================================ FILE: Dockerfile ================================================ FROM valian/docker-python-opencv-ffmpeg:py3 MAINTAINER Yasen Hu(yasenhu789@gmail.com) RUN apt-get update -y && \ apt-get install -y \ libvtk-java \ python-vtk \ tcl-vtk \ libvtk5-dev \ libvtk5-qt4-dev \ libusb-1.0-0-dev \ libeigen3-dev \ libboost-all-dev RUN apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* #Add new sudo user ARG USERNAME=yasen ARG UID=1000 ARG GID=1000 RUN useradd -m $USERNAME RUN usermod -aG sudo $USERNAME RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers # Replace 1000 with your user/group id RUN usermod --uid $UID $USERNAME && groupmod --gid $GID $USERNAME # Change user USER $USERNAME WORKDIR /home/$USERNAME ================================================ FILE: LICENSE ================================================ GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . ================================================ FILE: README.md ================================================ ## Introduction C++ implementation of SORT: Simple, online, and real-time tracking of multiple objects in a video sequence. Kuhn-Munkres (Hungarian) Algorithm in C++ is forked from: https://github.com/saebyn/munkres-cpp ## Dependencies - Ubuntu 16.04 - Docker 18.09.4 - OpenCV 3.4.2 - Boost 1.58.0 > All of the 3rd party libraries are included in the provided docker image ## Build Docker Image 1. Open Dockerfile, change line #19 ARG USERNAME to your host user name (echo $USER) 2. Open a terminal and run: ```bash $ cd /path/to/sort-cpp $ docker build -t sort . $ ./docker_run.sh ``` ## Demo: ![Screenshot-1](docs/1.png) To run the tracker with the provided detections and visualize the results: 1. Download the [2D MOT 2015 benchmark dataset](https://motchallenge.net/data/2D_MOT_2015/#download) 2. Create a symbolic link to the dataset ```bash $ ln -s /path/to/MOT2015_challenge/data/2DMOT2015 /path/to/sort-cpp/mot_benchmark ``` 3. Run the demo ```bash $ cd /path/to/sort-cpp $ mkdir build && cd "$_" $ cmake .. && make $ cd /path/to/sort-cpp/bin # Without display $ ./sort-cpp # With display $ ./sort-cpp -d ``` ## Evaluate Metrics Using the [Python implementation of metrics for benchmarking multiple object trackers (MOT)](https://github.com/cheind/py-motmetrics) to evaluate metrics. #### Dataset Structure ``` Layout for ground truth data //gt/gt.txt //gt/gt.txt ... Layout for test data /.txt /.txt ... Example: mot_benchmark ├── test │   ├── ADL-Rundle-6.txt │   └── ADL-Rundle-8.txt └── train ├── ADL-Rundle-6 │   └── gt │   └── gt.txt └── ADL-Rundle-8 └── gt └── gt.txt Sequences of ground truth and test will be matched according to the `` string. ``` #### Example ```bash # Optional for virtualenv $ source ~/env/bin/activate $ pip install motmetrics # Usage $ python -m motmetrics.apps.eval_motchallenge --help # Format: python -m motmetrics.apps.eval_motchallenge groundtruths tests $ python -m motmetrics.apps.eval_motchallenge mot_benchmark/train output/ ``` ## Result ![Screenshot-1](docs/2.png) FPS is around 1900 with Debug build. ## References 1. https://github.com/abewley/sort 2. https://github.com/mcximing/sort-cpp 3. https://github.com/saebyn/munkres-cpp ================================================ FILE: data/ADL-Rundle-6/det.txt ================================================ 1,-1,1691.97,381.048,152.23,352.617,0.995616,-1,-1,-1 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DOCKER_NAME=sort XSOCK=/tmp/.X11-unix XAUTH=/home/$USER/.Xauthority xhost + docker run \ -it --rm \ -e XAUTHORITY=${XAUTH} \ -e DISPLAY=${DISPLAY} \ -e QT_X11_NO_MITSHM=1 \ --privileged \ -v /dev/bus/usb:/dev/bus/usb \ -v /dev/video0:/dev/video0 \ -v $XSOCK:$XSOCK:rw \ -v $XAUTH:$XAUTH:rw \ -v /home/$USER:/home/$USER \ -u $(id -u ${USER}):$(id -g ${USER}) \ --name sort-cpp \ $DOCKER_NAME /bin/bash ================================================ FILE: include/kalman_filter.h ================================================ #pragma once #include #include // abstract class for Kalman filter // implementation could be KF/EKF/UKF... class KalmanFilter { public: /** * user need to define H matrix & R matrix * @param num_states * @param num_obs */ // constructor explicit KalmanFilter(unsigned int num_states, unsigned int num_obs); // destructor virtual ~KalmanFilter() = default; /** * Coast state and state covariance using the process model * User can use this function without change the internal * tracking state x_ */ virtual void Coast(); /** * Predict without measurement update */ void Predict(); /** * This function maps the true state space into the observed space * using the observation model * User can implement their own method for more complicated models */ virtual Eigen::VectorXd PredictionToObservation(const Eigen::VectorXd &state); /** * Updates the state by using Extended Kalman Filter equations * @param z The measurement at k+1 */ virtual void Update(const Eigen::VectorXd &z); /** * Calculate marginal log-likelihood to evaluate different parameter choices */ float CalculateLogLikelihood(const Eigen::VectorXd& y, const Eigen::MatrixXd& S); // State vector Eigen::VectorXd x_, x_predict_; // Error covariance matrix Eigen::MatrixXd P_, P_predict_; // State transition matrix Eigen::MatrixXd F_; // Covariance matrix of process noise Eigen::MatrixXd Q_; // measurement matrix Eigen::MatrixXd H_; // covariance matrix of observation noise Eigen::MatrixXd R_; unsigned int num_states_, num_obs_; float log_likelihood_delta_; float NIS_; }; ================================================ FILE: include/matrix.cpp ================================================ /* * Copyright (c) 2007 John Weaver * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include "matrix.h" #include #include #include /*export*/ template Matrix::Matrix() { m_rows = 0; m_columns = 0; m_matrix = nullptr; } /*export*/ template Matrix::Matrix(const std::initializer_list> init) { m_matrix = nullptr; m_rows = init.size(); if ( m_rows == 0 ) { m_columns = 0; } else { m_columns = init.begin()->size(); if ( m_columns > 0 ) { resize(m_rows, m_columns); } } size_t i = 0, j; for ( auto row = init.begin() ; row != init.end() ; ++row, ++i ) { assert ( row->size() == m_columns && "All rows must have the same number of columns." ); j = 0; for ( auto value = row->begin() ; value != row->end() ; ++value, ++j ) { m_matrix[i][j] = *value; } } } /*export*/ template Matrix::Matrix(const Matrix &other) { if ( other.m_matrix != nullptr ) { // copy arrays m_matrix = nullptr; resize(other.m_rows, other.m_columns); for ( size_t i = 0 ; i < m_rows ; i++ ) { for ( size_t j = 0 ; j < m_columns ; j++ ) { m_matrix[i][j] = other.m_matrix[i][j]; } } } else { m_matrix = nullptr; m_rows = 0; m_columns = 0; } } /*export*/ template Matrix::Matrix(const size_t rows, const size_t columns) { m_matrix = nullptr; resize(rows, columns); } /*export*/ template Matrix & Matrix::operator= (const Matrix &other) { if ( other.m_matrix != nullptr ) { // copy arrays resize(other.m_rows, other.m_columns); for ( size_t i = 0 ; i < m_rows ; i++ ) { for ( size_t j = 0 ; j < m_columns ; j++ ) { m_matrix[i][j] = other.m_matrix[i][j]; } } } else { // free arrays for ( size_t i = 0 ; i < m_columns ; i++ ) { delete [] m_matrix[i]; } delete [] m_matrix; m_matrix = nullptr; m_rows = 0; m_columns = 0; } return *this; } /*export*/ template Matrix::~Matrix() { if ( m_matrix != nullptr ) { // free arrays for ( size_t i = 0 ; i < m_rows ; i++ ) { delete [] m_matrix[i]; } delete [] m_matrix; } m_matrix = nullptr; } /*export*/ template void Matrix::resize(const size_t rows, const size_t columns, const T default_value) { assert ( rows > 0 && columns > 0 && "Columns and rows must exist." ); if ( m_matrix == nullptr ) { // alloc arrays m_matrix = new T*[rows]; // rows for ( size_t i = 0 ; i < rows ; i++ ) { m_matrix[i] = new T[columns]; // columns } m_rows = rows; m_columns = columns; clear(); } else { // save array pointer T **new_matrix; // alloc new arrays new_matrix = new T*[rows]; // rows for ( size_t i = 0 ; i < rows ; i++ ) { new_matrix[i] = new T[columns]; // columns for ( size_t j = 0 ; j < columns ; j++ ) { new_matrix[i][j] = default_value; } } // copy data from saved pointer to new arrays size_t minrows = std::min(rows, m_rows); size_t mincols = std::min(columns, m_columns); for ( size_t x = 0 ; x < minrows ; x++ ) { for ( size_t y = 0 ; y < mincols ; y++ ) { new_matrix[x][y] = m_matrix[x][y]; } } // delete old arrays if ( m_matrix != nullptr ) { for ( size_t i = 0 ; i < m_rows ; i++ ) { delete [] m_matrix[i]; } delete [] m_matrix; } m_matrix = new_matrix; } m_rows = rows; m_columns = columns; } /*export*/ template void Matrix::clear() { assert( m_matrix != nullptr ); for ( size_t i = 0 ; i < m_rows ; i++ ) { for ( size_t j = 0 ; j < m_columns ; j++ ) { m_matrix[i][j] = 0; } } } /*export*/ template inline T& Matrix::operator ()(const size_t x, const size_t y) { assert ( x < m_rows ); assert ( y < m_columns ); assert ( m_matrix != nullptr ); return m_matrix[x][y]; } /*export*/ template inline const T& Matrix::operator ()(const size_t x, const size_t y) const { assert ( x < m_rows ); assert ( y < m_columns ); assert ( m_matrix != nullptr ); return m_matrix[x][y]; } /*export*/ template const T Matrix::min() const { assert( m_matrix != nullptr ); assert ( m_rows > 0 ); assert ( m_columns > 0 ); T min = m_matrix[0][0]; for ( size_t i = 0 ; i < m_rows ; i++ ) { for ( size_t j = 0 ; j < m_columns ; j++ ) { min = std::min(min, m_matrix[i][j]); } } return min; } /*export*/ template const T Matrix::max() const { assert( m_matrix != nullptr ); assert ( m_rows > 0 ); assert ( m_columns > 0 ); T max = m_matrix[0][0]; for ( size_t i = 0 ; i < m_rows ; i++ ) { for ( size_t j = 0 ; j < m_columns ; j++ ) { max = std::max(max, m_matrix[i][j]); } } return max; } ================================================ FILE: include/matrix.h ================================================ /* * Copyright (c) 2007 John Weaver * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #ifndef _MATRIX_H_ #define _MATRIX_H_ #include #include #include template class Matrix { public: Matrix(); Matrix(const size_t rows, const size_t columns); Matrix(const std::initializer_list> init); Matrix(const Matrix &other); Matrix & operator= (const Matrix &other); ~Matrix(); // all operations modify the matrix in-place. void resize(const size_t rows, const size_t columns, const T default_value = 0); void clear(); T& operator () (const size_t x, const size_t y); const T& operator () (const size_t x, const size_t y) const; const T min() const; const T max() const; inline size_t minsize() { return ((m_rows < m_columns) ? m_rows : m_columns); } inline size_t columns() const { return m_columns;} inline size_t rows() const { return m_rows;} friend std::ostream& operator<<(std::ostream& os, const Matrix &matrix) { os << "Matrix:" << std::endl; for (size_t row = 0 ; row < matrix.rows() ; row++ ) { for (size_t col = 0 ; col < matrix.columns() ; col++ ) { os.width(8); os << matrix(row, col) << ","; } os << std::endl; } return os; } private: T **m_matrix; size_t m_rows; size_t m_columns; }; #ifndef USE_EXPORT_KEYWORD #include "matrix.cpp" //#define export /*export*/ #endif #endif /* !defined(_MATRIX_H_) */ ================================================ FILE: include/munkres.h ================================================ /* * Copyright (c) 2007 John Weaver * Copyright (c) 2015 Miroslav Krajicek * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #if !defined(_MUNKRES_H_) #define _MUNKRES_H_ #include "matrix.h" #include #include #include #include #include template class Munkres { static constexpr int NORMAL = 0; static constexpr int STAR = 1; static constexpr int PRIME = 2; public: /* * * Linear assignment problem solution * [modifies matrix in-place.] * matrix(row,col): row major format assumed. * * Assignments are remaining 0 values * (extra 0 values are replaced with -1) * */ void solve(Matrix &m) { const size_t rows = m.rows(), columns = m.columns(), size = std::max(rows, columns); #ifdef DEBUG std::cout << "Munkres input: " << m << std::endl; #endif // Copy input matrix this->matrix = m; if ( rows != columns ) { // If the input matrix isn't square, make it square // and fill the empty values with the largest value present // in the matrix. matrix.resize(size, size, matrix.max()); } // STAR == 1 == starred, PRIME == 2 == primed mask_matrix.resize(size, size); row_mask = new bool[size]; col_mask = new bool[size]; for ( size_t i = 0 ; i < size ; i++ ) { row_mask[i] = false; } for ( size_t i = 0 ; i < size ; i++ ) { col_mask[i] = false; } // Prepare the matrix values... // If there were any infinities, replace them with a value greater // than the maximum value in the matrix. replace_infinites(matrix); minimize_along_direction(matrix, rows >= columns); minimize_along_direction(matrix, rows < columns); // Follow the steps int step = 1; while ( step ) { switch ( step ) { case 1: step = step1(); // step is always 2 break; case 2: step = step2(); // step is always either 0 or 3 break; case 3: step = step3(); // step in [3, 4, 5] break; case 4: step = step4(); // step is always 2 break; case 5: step = step5(); // step is always 3 break; } } // Store results for ( size_t row = 0 ; row < size ; row++ ) { for ( size_t col = 0 ; col < size ; col++ ) { if ( mask_matrix(row, col) == STAR ) { matrix(row, col) = 0; } else { matrix(row, col) = -1; } } } #ifdef DEBUG std::cout << "Munkres output: " << matrix << std::endl; #endif // Remove the excess rows or columns that we added to fit the // input to a square matrix. matrix.resize(rows, columns); m = matrix; delete [] row_mask; delete [] col_mask; } static void replace_infinites(Matrix &matrix) { const size_t rows = matrix.rows(), columns = matrix.columns(); assert( rows > 0 && columns > 0 ); double max = matrix(0, 0); constexpr auto infinity = std::numeric_limits::infinity(); // Find the greatest value in the matrix that isn't infinity. for ( size_t row = 0 ; row < rows ; row++ ) { for ( size_t col = 0 ; col < columns ; col++ ) { if ( matrix(row, col) != infinity ) { if ( max == infinity ) { max = matrix(row, col); } else { max = std::max(max, matrix(row, col)); } } } } // a value higher than the maximum value present in the matrix. if ( max == infinity ) { // This case only occurs when all values are infinite. max = 0; } else { max++; } for ( size_t row = 0 ; row < rows ; row++ ) { for ( size_t col = 0 ; col < columns ; col++ ) { if ( matrix(row, col) == infinity ) { matrix(row, col) = max; } } } } static void minimize_along_direction(Matrix &matrix, const bool over_columns) { const size_t outer_size = over_columns ? matrix.columns() : matrix.rows(), inner_size = over_columns ? matrix.rows() : matrix.columns(); // Look for a minimum value to subtract from all values along // the "outer" direction. for ( size_t i = 0 ; i < outer_size ; i++ ) { double min = over_columns ? matrix(0, i) : matrix(i, 0); // As long as the current minimum is greater than zero, // keep looking for the minimum. // Start at one because we already have the 0th value in min. for ( size_t j = 1 ; j < inner_size && min > 0 ; j++ ) { min = std::min( min, over_columns ? matrix(j, i) : matrix(i, j)); } if ( min > 0 ) { for ( size_t j = 0 ; j < inner_size ; j++ ) { if ( over_columns ) { matrix(j, i) -= min; } else { matrix(i, j) -= min; } } } } } private: inline bool find_uncovered_in_matrix(const double item, size_t &row, size_t &col) const { const size_t rows = matrix.rows(), columns = matrix.columns(); for ( row = 0 ; row < rows ; row++ ) { if ( !row_mask[row] ) { for ( col = 0 ; col < columns ; col++ ) { if ( !col_mask[col] ) { if ( matrix(row,col) == item ) { return true; } } } } } return false; } bool pair_in_list(const std::pair &needle, const std::list > &haystack) { for ( std::list >::const_iterator i = haystack.begin() ; i != haystack.end() ; i++ ) { if ( needle == *i ) { return true; } } return false; } int step1() { const size_t rows = matrix.rows(), columns = matrix.columns(); for ( size_t row = 0 ; row < rows ; row++ ) { for ( size_t col = 0 ; col < columns ; col++ ) { if ( 0 == matrix(row, col) ) { for ( size_t nrow = 0 ; nrow < row ; nrow++ ) if ( STAR == mask_matrix(nrow,col) ) goto next_column; mask_matrix(row,col) = STAR; goto next_row; } next_column:; } next_row:; } return 2; } int step2() { const size_t rows = matrix.rows(), columns = matrix.columns(); size_t covercount = 0; for ( size_t row = 0 ; row < rows ; row++ ) for ( size_t col = 0 ; col < columns ; col++ ) if ( STAR == mask_matrix(row, col) ) { col_mask[col] = true; covercount++; } if ( covercount >= matrix.minsize() ) { #ifdef DEBUG std::cout << "Final cover count: " << covercount << std::endl; #endif return 0; } #ifdef DEBUG std::cout << "Munkres matrix has " << covercount << " of " << matrix.minsize() << " Columns covered:" << std::endl; std::cout << matrix << std::endl; #endif return 3; } int step3() { /* Main Zero Search 1. Find an uncovered Z in the distance matrix and prime it. If no such zero exists, go to Step 5 2. If No Z* exists in the row of the Z', go to Step 4. 3. If a Z* exists, cover this row and uncover the column of the Z*. Return to Step 3.1 to find a new Z */ if ( find_uncovered_in_matrix(0, saverow, savecol) ) { mask_matrix(saverow,savecol) = PRIME; // prime it. } else { return 5; } for ( size_t ncol = 0 ; ncol < matrix.columns() ; ncol++ ) { if ( mask_matrix(saverow,ncol) == STAR ) { row_mask[saverow] = true; //cover this row and col_mask[ncol] = false; // uncover the column containing the starred zero return 3; // repeat } } return 4; // no starred zero in the row containing this primed zero } int step4() { const size_t rows = matrix.rows(), columns = matrix.columns(); // seq contains pairs of row/column values where we have found // either a star or a prime that is part of the ``alternating sequence``. std::list > seq; // use saverow, savecol from step 3. std::pair z0(saverow, savecol); seq.insert(seq.end(), z0); // We have to find these two pairs: std::pair z1(-1, -1); std::pair z2n(-1, -1); size_t row, col = savecol; /* Increment Set of Starred Zeros 1. Construct the ``alternating sequence'' of primed and starred zeros: Z0 : Unpaired Z' from Step 4.2 Z1 : The Z* in the column of Z0 Z[2N] : The Z' in the row of Z[2N-1], if such a zero exists Z[2N+1] : The Z* in the column of Z[2N] The sequence eventually terminates with an unpaired Z' = Z[2N] for some N. */ bool madepair; do { madepair = false; for ( row = 0 ; row < rows ; row++ ) { if ( mask_matrix(row,col) == STAR ) { z1.first = row; z1.second = col; if ( pair_in_list(z1, seq) ) { continue; } madepair = true; seq.insert(seq.end(), z1); break; } } if ( !madepair ) break; madepair = false; for ( col = 0 ; col < columns ; col++ ) { if ( mask_matrix(row, col) == PRIME ) { z2n.first = row; z2n.second = col; if ( pair_in_list(z2n, seq) ) { continue; } madepair = true; seq.insert(seq.end(), z2n); break; } } } while ( madepair ); for ( std::list >::iterator i = seq.begin() ; i != seq.end() ; i++ ) { // 2. Unstar each starred zero of the sequence. if ( mask_matrix(i->first,i->second) == STAR ) mask_matrix(i->first,i->second) = NORMAL; // 3. Star each primed zero of the sequence, // thus increasing the number of starred zeros by one. if ( mask_matrix(i->first,i->second) == PRIME ) mask_matrix(i->first,i->second) = STAR; } // 4. Erase all primes, uncover all columns and rows, for ( size_t row = 0 ; row < mask_matrix.rows() ; row++ ) { for ( size_t col = 0 ; col < mask_matrix.columns() ; col++ ) { if ( mask_matrix(row,col) == PRIME ) { mask_matrix(row,col) = NORMAL; } } } for ( size_t i = 0 ; i < rows ; i++ ) { row_mask[i] = false; } for ( size_t i = 0 ; i < columns ; i++ ) { col_mask[i] = false; } // and return to Step 2. return 2; } int step5() { const size_t rows = matrix.rows(), columns = matrix.columns(); /* New Zero Manufactures 1. Let h be the smallest uncovered entry in the (modified) distance matrix. 2. Add h to all covered rows. 3. Subtract h from all uncovered columns 4. Return to Step 3, without altering stars, primes, or covers. */ double h = std::numeric_limits::max(); for ( size_t row = 0 ; row < rows ; row++ ) { if ( !row_mask[row] ) { for ( size_t col = 0 ; col < columns ; col++ ) { if ( !col_mask[col] ) { if ( h > matrix(row, col) && matrix(row, col) != 0 ) { h = matrix(row, col); } } } } } for ( size_t row = 0 ; row < rows ; row++ ) { if ( row_mask[row] ) { for ( size_t col = 0 ; col < columns ; col++ ) { matrix(row, col) += h; } } } for ( size_t col = 0 ; col < columns ; col++ ) { if ( !col_mask[col] ) { for ( size_t row = 0 ; row < rows ; row++ ) { matrix(row, col) -= h; } } } return 3; } Matrix mask_matrix; Matrix matrix; bool *row_mask; bool *col_mask; size_t saverow = 0, savecol = 0; }; #endif /* !defined(_MUNKRES_H_) */ ================================================ FILE: include/track.h ================================================ #pragma once #include #include "kalman_filter.h" class Track { public: // Constructor Track(); // Destructor ~Track() = default; void Init(const cv::Rect& bbox); void Predict(); void Update(const cv::Rect& bbox); cv::Rect GetStateAsBbox() const; float GetNIS() const; int coast_cycles_ = 0, hit_streak_ = 0; private: Eigen::VectorXd ConvertBboxToObservation(const cv::Rect& bbox) const; cv::Rect ConvertStateToBbox(const Eigen::VectorXd &state) const; KalmanFilter kf_; }; ================================================ FILE: include/tracker.h ================================================ #pragma once #include #include #include "track.h" #include "munkres.h" #include "utils.h" class Tracker { public: Tracker(); ~Tracker() = default; static float CalculateIou(const cv::Rect& det, const Track& track); static void HungarianMatching(const std::vector>& iou_matrix, size_t nrows, size_t ncols, std::vector>& association); /** * Assigns detections to tracked object (both represented as bounding boxes) * Returns 2 lists of matches, unmatched_detections * @param detection * @param tracks * @param matched * @param unmatched_det * @param iou_threshold */ static void AssociateDetectionsToTrackers(const std::vector& detection, std::map& tracks, std::map& matched, std::vector& unmatched_det, float iou_threshold = 0.3); void Run(const std::vector& detections); std::map GetTracks(); private: // Hash-map between ID and corresponding tracker std::map tracks_; // Assigned ID for each bounding box int id_; }; ================================================ FILE: include/utils.h ================================================ #pragma once constexpr int kNumColors = 32; constexpr int kMaxCoastCycles = 1; constexpr int kMinHits = 3; // Set threshold to 0 to accept all detections constexpr float kMinConfidence = 0.6; ================================================ FILE: src/kalman_filter.cpp ================================================ #include "kalman_filter.h" KalmanFilter::KalmanFilter(unsigned int num_states, unsigned int num_obs) : num_states_(num_states), num_obs_(num_obs) { /*** Predict ***/ // State vector x_ = Eigen::VectorXd::Zero(num_states); // Predicted(a prior) state vector x_predict_ = Eigen::VectorXd::Zero(num_states); // State transition matrix F_ F_ = Eigen::MatrixXd::Zero(num_states, num_states); // Error covariance matrix P P_ = Eigen::MatrixXd::Zero(num_states, num_states); // Predicted(a prior) error covariance matrix P_predict_ = Eigen::MatrixXd::Zero(num_states, num_states); // Covariance matrix of process noise Q_ = Eigen::MatrixXd::Zero(num_states, num_states); /*** Update ***/ // Observation matrix H_ = Eigen::MatrixXd::Zero(num_obs, num_states); // Covariance matrix of observation noise R_ = Eigen::MatrixXd::Zero(num_obs, num_obs); log_likelihood_delta_ = 0.0; NIS_ = 0.0; } void KalmanFilter::Coast() { x_predict_ = F_ * x_; P_predict_ = F_ * P_ * F_.transpose() + Q_; } void KalmanFilter::Predict() { Coast(); x_ = x_predict_; P_ = P_predict_; } Eigen::VectorXd KalmanFilter::PredictionToObservation(const Eigen::VectorXd &state) { return (H_*state); } void KalmanFilter::Update(const Eigen::VectorXd& z) { Eigen::VectorXd z_predict = PredictionToObservation(x_predict_); // y - innovation, z - real observation, z_predict - predicted observation Eigen::VectorXd y = z - z_predict; Eigen::MatrixXd Ht = H_.transpose(); // S - innovation covariance Eigen::MatrixXd S = H_ * P_predict_ * Ht + R_; NIS_ = y.transpose() * S.inverse() * y; // std::cout << std::endl; // std::cout << "P_predict = " << std::endl; // std::cout << P_predict_ << std::endl; // // // std::cout << "Z = " << std::endl; // std::cout << z << std::endl; // // std::cout << "Z_pred = " << std::endl; // std::cout << z_predict << std::endl; // // std::cout << "y = " << std::endl; // std::cout << y << std::endl; // // std::cout << "S = " << std::endl; // std::cout << S << std::endl; // // std::cout << "NIS = " << NIS_ << std::endl; // K - Kalman gain Eigen::MatrixXd K = P_predict_ * Ht * S.inverse(); // Updated state estimation x_ = x_predict_ + K * y; Eigen::MatrixXd I = Eigen::MatrixXd::Identity(num_states_, num_states_); // Joseph form //P_ = (I - K * H_) * P_predict_ * (I - K * H_).transpose() + K * R_ * K.transpose(); // Optimal gain P_ = (I - K * H_) * P_predict_; } float KalmanFilter::CalculateLogLikelihood(const Eigen::VectorXd& y, const Eigen::MatrixXd& S) { float log_likelihood; // Note: Computing log(M.determinant()) in Eigen C++ is risky for large matrices since it may overflow or underflow. // compute the Cholesky decomposition of the innovation covariance matrix, because it is symmetric // S = L * L^T = U^T * U // then retrieve factor L in the decomposition auto& L = S.llt().matrixL(); // find log determinant of innovation covariance matrix float log_determinant = 0; for (unsigned int i = 0; i < S.rows(); i++) log_determinant += log(L(i, i)); log_determinant *= 2; // log-likelihood expression for current iteration log_likelihood = -0.5 * (y.transpose() * S.inverse() * y + num_obs_ * log(2 * M_PI) + log_determinant); if (std::isnan(log_likelihood)) { log_likelihood = -1e50; } return log_likelihood; } ================================================ FILE: src/main.cpp ================================================ /** * SORT: A Simple, Online and Realtime Tracker */ #include #include #include #include #include #include #include #include #include #include #include "tracker.h" #include "utils.h" std::vector> ProcessLabel(std::ifstream& label_file) { // Process labels - group bounding boxes by frame index std::vector> bbox; std::vector bbox_per_frame; // Label index starts from 1 int current_frame_index = 1; std::string line; while (std::getline(label_file, line)) { std::stringstream ss(line); // Label format , , , , , , , , , std::vector label; std::string data; while (getline(ss , data, ',')) { label.push_back(std::stof(data)); } if (static_cast(label[0]) != current_frame_index) { current_frame_index = static_cast(label[0]); bbox.push_back(bbox_per_frame); bbox_per_frame.clear(); } // Ignore low confidence detections if (label[6] > kMinConfidence) { bbox_per_frame.emplace_back(label[2], label[3], label[4], label[5]); } } // Add bounding boxes from last frame bbox.push_back(bbox_per_frame); return bbox; } int main(int argc, const char *argv[]) { // parse program input arguments boost::program_options::options_description desc{"Options"}; desc.add_options() ("help,h", "Help screen") ("display,d", "Display online tracker output (slow) [False]"); boost::program_options::variables_map vm; boost::program_options::store(parse_command_line(argc, argv, desc), vm); boost::program_options::notify(vm); if (vm.count("help")) { std::cout << desc << '\n'; return -1; } bool enable_display_flag = false; if (vm.count("display")) { enable_display_flag = true; } std::vector colors; if (enable_display_flag) { // Create a window to display original image cv::namedWindow("Original", cv::WINDOW_AUTOSIZE); // Create a window to display tracking result cv::namedWindow("Tracking", cv::WINDOW_AUTOSIZE); // Generate random colors to visualize different bbox std::random_device rd; //Will be used to obtain a seed for the random number engine std::mt19937 gen(rd()); //Standard mersenne_twister_engine seeded with rd() constexpr int max_random_value = 20; std::uniform_int_distribution<> dis(0, max_random_value); constexpr int factor = 255 / max_random_value; for (int n = 0; n < kNumColors; ++n) { //Use dis to transform the random unsigned int generated by gen into an int in [0, 7] colors.emplace_back(cv::Scalar(dis(gen) * factor, dis(gen) * factor, dis(gen) * factor)); } } // All training dataset in MOT15 std::vector dataset_names{"ADL-Rundle-6", "ADL-Rundle-8", "ETH-Bahnhof", "ETH-Pedcross2", "ETH-Sunnyday", "KITTI-13", "KITTI-17", "PETS09-S2L1", "TUD-Campus", "TUD-Stadtmitte", "Venice-2"}; // create SORT tracker Tracker tracker; for (const auto& dataset_name : dataset_names) { // Open label file and load detections from MOT dataset // Note that it can also be replaced by detections from you own detector std::string label_path = "../data/" + dataset_name + "/det.txt"; std::ifstream label_file(label_path); if (!label_file.is_open()) { std::cerr << "Could not open or find the label!!!" << std::endl; return -1; } std::vector> all_detections = ProcessLabel(label_file); // Close label file label_file.close(); // Load image paths for visualization std::vector images; if (enable_display_flag) { // Load images cv::String path("../mot_benchmark/train/" + dataset_name + "/img1/*.jpg"); // Non-recursive cv::glob(path, images); } // Create output folder if it does not exist std::string output_folder = "../output/"; boost::filesystem::path output_folder_path(output_folder); if(boost::filesystem::create_directory(output_folder_path)) { std::cerr<< "Directory Created: "<< output_folder <= kMinHits || frame_index < kMinHits)) { // Print to terminal for debugging std::cout << frame_index << "," << trk.first << "," << bbox.tl().x << "," << bbox.tl().y << "," << bbox.width << "," << bbox.height << ",1,-1,-1,-1" << " Hit Streak = " << trk.second.hit_streak_ << " Coast Cycles = " << trk.second.coast_cycles_ << std::endl; // Export to text file for metrics evaluation output_file << frame_index << "," << trk.first << "," << bbox.tl().x << "," << bbox.tl().y << "," << bbox.width << "," << bbox.height << ",1,-1,-1,-1\n"; // output_file_NIS << trk.second.GetNIS() << "\n"; } } // Visualize tracking result if (enable_display_flag) { // Read image file cv::Mat img = cv::imread(images[i]); // Make a copy for display cv::Mat img_tracking = img.clone(); // Check for invalid input if (img.empty()) { std::cerr << "Could not open or find the image!!!" << std::endl; return -1; } for (const auto &det : detections) { // Draw detections in red bounding box cv::rectangle(img, det, cv::Scalar(0, 0, 255), 3); } for (auto &trk : tracks) { // only draw tracks which meet certain criteria if (trk.second.coast_cycles_ < kMaxCoastCycles && (trk.second.hit_streak_ >= kMinHits || frame_index < kMinHits)) { const auto &bbox = trk.second.GetStateAsBbox(); cv::putText(img_tracking, std::to_string(trk.first), cv::Point(bbox.tl().x, bbox.tl().y - 10), cv::FONT_HERSHEY_DUPLEX, 2, cv::Scalar(255, 255, 255), 2); cv::rectangle(img_tracking, bbox, colors[trk.first % kNumColors], 3); } } // Show detection and tracking result cv::imshow("Original", img); cv::imshow("Tracking", img_tracking); // Delay in ms auto key = cv::waitKey(33); // Exit if ESC pressed if (27 == key) { return 0; } else if (32 == key) { // Press Space to pause and press it again to resume while (true) { key = cv::waitKey(0); if (32 == key) { break; } else if (27 == key) { return 0; } } } } // end of enable_display_flag } // end of iterating all frames auto t2 = std::chrono::high_resolution_clock::now(); std::chrono::duration time_span = std::chrono::duration_cast>(t2 - t1); std::cout << "********************************" << std::endl; std::cout << "Total tracking took: " << time_span.count() << " for " << total_frames << " frames" << std::endl; std::cout << "FPS = " << total_frames / time_span.count() << std::endl; if (enable_display_flag) { std::cout << "Note: to get real runtime results run without the option: --display" << std::endl; } std::cout << "********************************" << std::endl; output_file.close(); } // end of iterating all dataset return 0; } ================================================ FILE: src/munkres.cpp ================================================ /* * Copyright (c) 2007 John Weaver * Copyright (c) 2015 Miroslav Krajicek * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA */ #include "munkres.h" template class Munkres; template class Munkres; template class Munkres; ================================================ FILE: src/track.cpp ================================================ #include "track.h" Track::Track() : kf_(8, 4) { /*** Define constant velocity model ***/ // state - center_x, center_y, width, height, v_cx, v_cy, v_width, v_height kf_.F_ << 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1; // Give high uncertainty to the unobservable initial velocities kf_.P_ << 10, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 10000, 0, 0, 0, 0, 0, 0, 0, 0, 10000, 0, 0, 0, 0, 0, 0, 0, 0, 10000, 0, 0, 0, 0, 0, 0, 0, 0, 10000; kf_.H_ << 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0; kf_.Q_ << 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0.0001, 0, 0, 0, 0, 0, 0, 0, 0, 0.0001; kf_.R_ << 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 10, 0, 0, 0, 0, 10; } // Get predicted locations from existing trackers // dt is time elapsed between the current and previous measurements void Track::Predict() { kf_.Predict(); // hit streak count will be reset if (coast_cycles_ > 0) { hit_streak_ = 0; } // accumulate coast cycle count coast_cycles_++; } // Update matched trackers with assigned detections void Track::Update(const cv::Rect& bbox) { // get measurement update, reset coast cycle count coast_cycles_ = 0; // accumulate hit streak count hit_streak_++; // observation - center_x, center_y, area, ratio Eigen::VectorXd observation = ConvertBboxToObservation(bbox); kf_.Update(observation); } // Create and initialize new trackers for unmatched detections, with initial bounding box void Track::Init(const cv::Rect &bbox) { kf_.x_.head(4) << ConvertBboxToObservation(bbox); hit_streak_++; } /** * Returns the current bounding box estimate * @return */ cv::Rect Track::GetStateAsBbox() const { return ConvertStateToBbox(kf_.x_); } float Track::GetNIS() const { return kf_.NIS_; } /** * Takes a bounding box in the form [x, y, width, height] and returns z in the form * [x, y, s, r] where x,y is the centre of the box and s is the scale/area and r is * the aspect ratio * * @param bbox * @return */ Eigen::VectorXd Track::ConvertBboxToObservation(const cv::Rect& bbox) const{ Eigen::VectorXd observation = Eigen::VectorXd::Zero(4); auto width = static_cast(bbox.width); auto height = static_cast(bbox.height); float center_x = bbox.x + width / 2; float center_y = bbox.y + height / 2; observation << center_x, center_y, width, height; return observation; } /** * Takes a bounding box in the centre form [x,y,s,r] and returns it in the form * [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right * * @param state * @return */ cv::Rect Track::ConvertStateToBbox(const Eigen::VectorXd &state) const { // state - center_x, center_y, width, height, v_cx, v_cy, v_width, v_height auto width = std::max(0, static_cast(state[2])); auto height = std::max(0, static_cast(state[3])); auto tl_x = static_cast(state[0] - width / 2.0); auto tl_y = static_cast(state[1] - height / 2.0); cv::Rect rect(cv::Point(tl_x, tl_y), cv::Size(width, height)); return rect; } ================================================ FILE: src/tracker.cpp ================================================ #include "tracker.h" Tracker::Tracker() { id_ = 0; } float Tracker::CalculateIou(const cv::Rect& det, const Track& track) { auto trk = track.GetStateAsBbox(); // get min/max points auto xx1 = std::max(det.tl().x, trk.tl().x); auto yy1 = std::max(det.tl().y, trk.tl().y); auto xx2 = std::min(det.br().x, trk.br().x); auto yy2 = std::min(det.br().y, trk.br().y); auto w = std::max(0, xx2 - xx1); auto h = std::max(0, yy2 - yy1); // calculate area of intersection and union float det_area = det.area(); float trk_area = trk.area(); auto intersection_area = w * h; float union_area = det_area + trk_area - intersection_area; auto iou = intersection_area / union_area; return iou; } void Tracker::HungarianMatching(const std::vector>& iou_matrix, size_t nrows, size_t ncols, std::vector>& association) { Matrix matrix(nrows, ncols); // Initialize matrix with IOU values for (size_t i = 0 ; i < nrows ; i++) { for (size_t j = 0 ; j < ncols ; j++) { // Multiply by -1 to find max cost if (iou_matrix[i][j] != 0) { matrix(i, j) = -iou_matrix[i][j]; } else { // TODO: figure out why we have to assign value to get correct result matrix(i, j) = 1.0f; } } } // // Display begin matrix state. // for (size_t row = 0 ; row < nrows ; row++) { // for (size_t col = 0 ; col < ncols ; col++) { // std::cout.width(10); // std::cout << matrix(row,col) << ","; // } // std::cout << std::endl; // } // std::cout << std::endl; // Apply Kuhn-Munkres algorithm to matrix. Munkres m; m.solve(matrix); // // Display solved matrix. // for (size_t row = 0 ; row < nrows ; row++) { // for (size_t col = 0 ; col < ncols ; col++) { // std::cout.width(2); // std::cout << matrix(row,col) << ","; // } // std::cout << std::endl; // } // std::cout << std::endl; for (size_t i = 0 ; i < nrows ; i++) { for (size_t j = 0 ; j < ncols ; j++) { association[i][j] = matrix(i, j); } } } void Tracker::AssociateDetectionsToTrackers(const std::vector& detection, std::map& tracks, std::map& matched, std::vector& unmatched_det, float iou_threshold) { // Set all detection as unmatched if no tracks existing if (tracks.empty()) { for (const auto& det : detection) { unmatched_det.push_back(det); } return; } std::vector> iou_matrix; // resize IOU matrix based on number of detection and tracks iou_matrix.resize(detection.size(), std::vector(tracks.size())); std::vector> association; // resize association matrix based on number of detection and tracks association.resize(detection.size(), std::vector(tracks.size())); // row - detection, column - tracks for (size_t i = 0; i < detection.size(); i++) { size_t j = 0; for (const auto& trk : tracks) { iou_matrix[i][j] = CalculateIou(detection[i], trk.second); j++; } } // Find association HungarianMatching(iou_matrix, detection.size(), tracks.size(), association); for (size_t i = 0; i < detection.size(); i++) { bool matched_flag = false; size_t j = 0; for (const auto& trk : tracks) { if (0 == association[i][j]) { // Filter out matched with low IOU if (iou_matrix[i][j] >= iou_threshold) { matched[trk.first] = detection[i]; matched_flag = true; } // It builds 1 to 1 association, so we can break from here break; } j++; } // if detection cannot match with any tracks if (!matched_flag) { unmatched_det.push_back(detection[i]); } } } void Tracker::Run(const std::vector& detections) { /*** Predict internal tracks from previous frame ***/ for (auto &track : tracks_) { track.second.Predict(); } // Hash-map between track ID and associated detection bounding box std::map matched; // vector of unassociated detections std::vector unmatched_det; // return values - matched, unmatched_det if (!detections.empty()) { AssociateDetectionsToTrackers(detections, tracks_, matched, unmatched_det); } /*** Update tracks with associated bbox ***/ for (const auto &match : matched) { const auto &ID = match.first; tracks_[ID].Update(match.second); } /*** Create new tracks for unmatched detections ***/ for (const auto &det : unmatched_det) { Track tracker; tracker.Init(det); // Create new track and generate new ID tracks_[id_++] = tracker; } /*** Delete lose tracked tracks ***/ for (auto it = tracks_.begin(); it != tracks_.end();) { if (it->second.coast_cycles_ > kMaxCoastCycles) { it = tracks_.erase(it); } else { it++; } } } std::map Tracker::GetTracks() { return tracks_; }