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/*
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Tracker based on Kernelized Correlation Filter (KCF) [1] and Circulant Structure with Kernels (CSK) [2].
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CSK is implemented by using raw gray level features, since it is a single-channel filter.
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KCF is implemented by using HOG features (the default), since it extends CSK to multiple channels.
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[1] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,
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"High-Speed Tracking with Kernelized Correlation Filters", TPAMI 2015.
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[2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,
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"Exploiting the Circulant Structure of Tracking-by-detection with Kernels", ECCV 2012.
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Authors: Joao Faro, Christian Bailer, Joao F. Henriques
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Contacts: joaopfaro@gmail.com, Christian.Bailer@dfki.de, henriques@isr.uc.pt
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Institute of Systems and Robotics - University of Coimbra / Department Augmented Vision DFKI
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Constructor parameters, all boolean:
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hog: use HOG features (default), otherwise use raw pixels
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fixed_window: fix window size (default), otherwise use ROI size (slower but more accurate)
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multiscale: use multi-scale tracking (default; cannot be used with fixed_window = true)
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Default values are set for all properties of the tracker depending on the above choices.
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Their values can be customized further before calling init():
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interp_factor: linear interpolation factor for adaptation
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sigma: gaussian kernel bandwidth
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lambda: regularization
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cell_size: HOG cell size
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padding: area surrounding the target, relative to its size
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output_sigma_factor: bandwidth of gaussian target
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template_size: template size in pixels, 0 to use ROI size
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scale_step: scale step for multi-scale estimation, 1 to disable it
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scale_weight: to downweight detection scores of other scales for added stability
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For speed, the value (template_size/cell_size) should be a power of 2 or a product of small prime numbers.
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Inputs to init():
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image is the initial frame.
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roi is a cv::Rect with the target positions in the initial frame
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Inputs to update():
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image is the current frame.
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Outputs of update():
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cv::Rect with target positions for the current frame
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By downloading, copying, installing or using the software you agree to this license.
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If you do not agree to this license, do not download, install,
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copy or use the software.
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License Agreement
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For Open Source Computer Vision Library
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(3-clause BSD License)
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Redistribution and use in source and binary forms, with or without modification,
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are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice,
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this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the names of the copyright holders nor the names of the contributors
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may be used to endorse or promote products derived from this software
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without specific prior written permission.
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This software is provided by the copyright holders and contributors "as is" and
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any express or implied warranties, including, but not limited to, the implied
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warranties of merchantability and fitness for a particular purpose are disclaimed.
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In no event shall copyright holders or contributors be liable for any direct,
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indirect, incidental, special, exemplary, or consequential damages
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(including, but not limited to, procurement of substitute goods or services;
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loss of use, data, or profits; or business interruption) however caused
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and on any theory of liability, whether in contract, strict liability,
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or tort (including negligence or otherwise) arising in any way out of
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the use of this software, even if advised of the possibility of such damage.
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*/
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//#ifndef _KCFTRACKER_HEADERS
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#include "stdafx.h"
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#include "kcftracker.hpp"
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#include "ffttools.hpp"
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#include "recttools.hpp"
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#include "fhog.hpp"
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#include "labdata.hpp"
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//#endif
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// Constructor
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KCFTracker::KCFTracker(bool hog, bool fixed_window, bool multiscale, bool lab)
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{
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// Parameters equal in all cases
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lambda = 0.0001;
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padding = 2.5;
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//output_sigma_factor = 0.1;
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output_sigma_factor = 0.125;
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if (hog) { // HOG
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// VOT
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interp_factor = 0.012;
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sigma = 0.6;
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// TPAMI
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//interp_factor = 0.02;
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//sigma = 0.5;
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cell_size = 4;
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_hogfeatures = true;
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if (lab) {
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interp_factor = 0.005;
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sigma = 0.4;
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//output_sigma_factor = 0.025;
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output_sigma_factor = 0.1;
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_labfeatures = true;
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_labCentroids = cv::Mat(nClusters, 3, CV_32FC1, &data);
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cell_sizeQ = cell_size*cell_size;
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}
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else{
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_labfeatures = false;
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}
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}
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else { // RAW
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interp_factor = 0.075;
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sigma = 0.2;
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cell_size = 1;
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_hogfeatures = false;
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if (lab) {
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printf("Lab features are only used with HOG features.\n");
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_labfeatures = false;
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}
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}
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if (multiscale) { // multiscale
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template_size = 96;
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//template_size = 100;
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scale_step = 1.05;
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scale_weight = 0.95;
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if (!fixed_window) {
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//printf("Multiscale does not support non-fixed window.\n");
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fixed_window = true;
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}
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}
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else if (fixed_window) { // fit correction without multiscale
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template_size = 96;
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//template_size = 100;
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scale_step = 1;
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}
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else {
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template_size = 1;
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scale_step = 1;
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}
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}
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// Initialize tracker
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void KCFTracker::init(const cv::Rect &roi, cv::Mat image)
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{
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_roi = roi;
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assert(roi.width >= 0 && roi.height >= 0);
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_tmpl = getFeatures(image, 1);//只有第一次初始化的时候，第二个形参才为1，对第一帧特征进行汉宁窗平滑
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_prob = createGaussianPeak(size_patch[0], size_patch[1]);//创建高斯峰，只有第一帧才用到
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_alphaf = cv::Mat(size_patch[0], size_patch[1], CV_32FC2, float(0));////alphaf初始化
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//_num = cv::Mat(size_patch[0], size_patch[1], CV_32FC2, float(0));
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//_den = cv::Mat(size_patch[0], size_patch[1], CV_32FC2, float(0));
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train(_tmpl, 1.0); // train with initial frame
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}
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// Update position based on the new frame
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cv::Rect KCFTracker::update(cv::Mat image)
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{
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if (_roi.x + _roi.width <= 0) _roi.x = -_roi.width + 1;
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if (_roi.y + _roi.height <= 0) _roi.y = -_roi.height + 1;
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if (_roi.x >= image.cols - 1) _roi.x = image.cols - 2;
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if (_roi.y >= image.rows - 1) _roi.y = image.rows - 2;
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float cx = _roi.x + _roi.width / 2.0f;//中心点
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float cy = _roi.y + _roi.height / 2.0f;
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float peak_value;
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cv::Point2f res = detect(_tmpl, getFeatures(image, 0, 1.0f), peak_value);//获取response的位置
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//处理尺度变化的情况
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if (scale_step != 1) {
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// Test at a smaller _scale
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float new_peak_value;
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cv::Point2f new_res = detect(_tmpl, getFeatures(image, 0, 1.0f / scale_step), new_peak_value);
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//update roi and other parameter 更新roi区域及其参数
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if (scale_weight * new_peak_value > peak_value) {
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res = new_res;
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peak_value = new_peak_value;
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_scale /= scale_step;
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_roi.width /= scale_step;
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_roi.height /= scale_step;
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}
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// Test at a bigger _scale
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new_res = detect(_tmpl, getFeatures(image, 0, scale_step), new_peak_value);
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if (scale_weight * new_peak_value > peak_value) {
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res = new_res;
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peak_value = new_peak_value;
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_scale *= scale_step;
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_roi.width *= scale_step;
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_roi.height *= scale_step;
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}
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}
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// Adjust by cell size and _scale ????????????cell size????????????????????
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_roi.x = cx - _roi.width / 2.0f + ((float) res.x * cell_size * _scale);
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_roi.y = cy - _roi.height / 2.0f + ((float) res.y * cell_size * _scale);
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//超出边界的情况
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if (_roi.x >= image.cols - 1) _roi.x = image.cols - 1;
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if (_roi.y >= image.rows - 1) _roi.y = image.rows - 1;
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if (_roi.x + _roi.width <= 0) _roi.x = -_roi.width + 2;
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if (_roi.y + _roi.height <= 0) _roi.y = -_roi.height + 2;
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assert(_roi.width >= 0 && _roi.height >= 0);
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//上面得到roi新的位置之后，再重新训练得到相关滤波器
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cv::Mat x = getFeatures(image, 0);//提取新的roi特征
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train(x, interp_factor);//训练得到新的滤波器
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return _roi;
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}
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// Detect object in the current frame.
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cv::Point2f KCFTracker::detect(cv::Mat z, cv::Mat x, float &peak_value)
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{
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using namespace FFTTools;
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cv::Mat k = gaussianCorrelation(x, z);//作相关运算
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cv::Mat res = (real(fftd(complexMultiplication(_alphaf, fftd(k)), true)));//获得response
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//minMaxLoc only accepts doubles for the peak, and integer points for the coordinates
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cv::Point2i pi;//存放响应response最大值所在的位置
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double pv;//pv存放响应response最大值
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//找到输入数组的最大/最小值，此处寻找最大值，pv存放最大值，pi存放最大值所在的位置
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cv::minMaxLoc(res, NULL, &pv, NULL, &pi);
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peak_value = (float) pv;
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//subpixel peak estimation, coordinates will be non-integer
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cv::Point2f p((float)pi.x, (float)pi.y);
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//
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//target location is at the maximum response. we must take into
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//account the fact that, if the target doesn't move, the peak
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//will appear at the top - left corner, not at the center(this is
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//discussed in the paper).the responses wrap around cyclically.
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if (pi.x > 0 && pi.x < res.cols-1) {
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p.x += subPixelPeak(res.at<float>(pi.y, pi.x-1), peak_value, res.at<float>(pi.y, pi.x+1));
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}
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if (pi.y > 0 && pi.y < res.rows-1) {
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p.y += subPixelPeak(res.at<float>(pi.y-1, pi.x), peak_value, res.at<float>(pi.y+1, pi.x));
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}
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p.x -= (res.cols) / 2;
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p.y -= (res.rows) / 2;
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return p; //responses最大响应对应的目标位置
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}
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// train tracker with a single image
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void KCFTracker::train(cv::Mat x, float train_interp_factor)
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{
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using namespace FFTTools;
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cv::Mat k = gaussianCorrelation(x, x);
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cv::Mat alphaf = complexDivision(_prob, (fftd(k) + lambda));
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_tmpl = (1 - train_interp_factor) * _tmpl + (train_interp_factor) * x;
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_alphaf = (1 - train_interp_factor) * _alphaf + (train_interp_factor) * alphaf;
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/*cv::Mat kf = fftd(gaussianCorrelation(x, x));
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cv::Mat num = complexMultiplication(kf, _prob);
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cv::Mat den = complexMultiplication(kf, kf + lambda);
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_tmpl = (1 - train_interp_factor) * _tmpl + (train_interp_factor) * x;
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_num = (1 - train_interp_factor) * _num + (train_interp_factor) * num;
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_den = (1 - train_interp_factor) * _den + (train_interp_factor) * den;
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_alphaf = complexDivision(_num, _den);*/
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}
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// Evaluates a Gaussian kernel with bandwidth SIGMA for all relative shifts between input images X and Y,
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// which must both be MxN. They must also be periodic (ie., pre-processed with a cosine window).
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cv::Mat KCFTracker::gaussianCorrelation(cv::Mat x1, cv::Mat x2)
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{
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using namespace FFTTools;
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cv::Mat c = cv::Mat( cv::Size(size_patch[1], size_patch[0]), CV_32F, cv::Scalar(0) );
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// HOG features
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if (_hogfeatures) {
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cv::Mat caux;
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cv::Mat x1aux;
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cv::Mat x2aux;
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for (int i = 0; i < size_patch[2]; i++) {
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x1aux = x1.row(i); // Procedure do deal with cv::Mat multichannel bug
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x1aux = x1aux.reshape(1, size_patch[0]);
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x2aux = x2.row(i).reshape(1, size_patch[0]);
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cv::mulSpectrums(fftd(x1aux), fftd(x2aux), caux, 0, true);
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caux = fftd(caux, true);
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rearrange(caux);
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caux.convertTo(caux,CV_32F);
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c = c + real(caux);
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}
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}
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// Gray features
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else {
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cv::mulSpectrums(fftd(x1), fftd(x2), c, 0, true);
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c = fftd(c, true);
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rearrange(c);
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c = real(c);
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}
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cv::Mat d;
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cv::max(( (cv::sum(x1.mul(x1))[0] + cv::sum(x2.mul(x2))[0])- 2. * c) / (size_patch[0]*size_patch[1]*size_patch[2]) , 0, d);
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cv::Mat k;
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cv::exp((-d / (sigma * sigma)), k);
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return k;
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}
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// Create Gaussian Peak. Function called only in the first frame.
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cv::Mat KCFTracker::createGaussianPeak(int sizey, int sizex)
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{
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cv::Mat_<float> res(sizey, sizex);
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int syh = (sizey) / 2;
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int sxh = (sizex) / 2;
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float output_sigma = std::sqrt((float) sizex * sizey) / padding * output_sigma_factor;
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float mult = -0.5 / (output_sigma * output_sigma);
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for (int i = 0; i < sizey; i++)
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for (int j = 0; j < sizex; j++)
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{
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int ih = i - syh;
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int jh = j - sxh;
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res(i, j) = std::exp(mult * (float) (ih * ih + jh * jh));
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}
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return FFTTools::fftd(res);
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}
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// Obtain sub-window from image, with replication-padding and extract features
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cv::Mat KCFTracker::getFeatures(const cv::Mat & image, bool inithann, float scale_adjust)
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{
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cv::Rect extracted_roi;
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float cx = _roi.x + _roi.width / 2;
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float cy = _roi.y + _roi.height / 2;
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if (inithann) {//汉宁窗??????
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int padded_w = _roi.width * padding;
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int padded_h = _roi.height * padding;
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if (template_size > 1) { // Fit largest dimension to the given template size
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if (padded_w >= padded_h) //fit to width
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_scale = padded_w / (float) template_size;
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else
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_scale = padded_h / (float) template_size;
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_tmpl_sz.width = padded_w / _scale;
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_tmpl_sz.height = padded_h / _scale;
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}
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else { //No template size given, use ROI size
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_tmpl_sz.width = padded_w;
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_tmpl_sz.height = padded_h;
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_scale = 1;
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// original code from paper:
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/*if (sqrt(padded_w * padded_h) >= 100) { //Normal size
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_tmpl_sz.width = padded_w;
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_tmpl_sz.height = padded_h;
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_scale = 1;
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}
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else { //ROI is too big, track at half size
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_tmpl_sz.width = padded_w / 2;
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_tmpl_sz.height = padded_h / 2;
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_scale = 2;
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}*/
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}
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if (_hogfeatures) {
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// Round to cell size and also make it even
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_tmpl_sz.width = ( ( (int)(_tmpl_sz.width / (2 * cell_size)) ) * 2 * cell_size ) + cell_size*2;
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_tmpl_sz.height = ( ( (int)(_tmpl_sz.height / (2 * cell_size)) ) * 2 * cell_size ) + cell_size*2;
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}
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else { //Make number of pixels even (helps with some logic involving half-dimensions)
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_tmpl_sz.width = (_tmpl_sz.width / 2) * 2;
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_tmpl_sz.height = (_tmpl_sz.height / 2) * 2;
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}
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}
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extracted_roi.width = scale_adjust * _scale * _tmpl_sz.width;
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extracted_roi.height = scale_adjust * _scale * _tmpl_sz.height;
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|
|
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// center roi with new size
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extracted_roi.x = cx - extracted_roi.width / 2;
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extracted_roi.y = cy - extracted_roi.height / 2;
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cv::Mat FeaturesMap;
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|
|
//obtain a subwindow for detection at the position from last
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// frame, and convert to Fourier domain(its size is unchanged)
|
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// 在本帧中获取前一帧目标位置的子窗口
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cv::Mat z = RectTools::subwindow(image, extracted_roi, cv::BORDER_REPLICATE);
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//然后要对z提取特征，然后再到频域上与相关滤波器作相关，得到response之后产生新的目标位置
|
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//////////////////////////////////////////////////////////////
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///////////////////////////////////////////////////////////////
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if (z.cols != _tmpl_sz.width || z.rows != _tmpl_sz.height) {
|
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|
cv::resize(z, z, _tmpl_sz);
|
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}
|
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|
|
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|
// HOG features
|
|
|
if (_hogfeatures) {
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IplImage z_ipl = z;
|
|
|
CvLSVMFeatureMapCaskade *map;
|
|
|
getFeatureMaps(&z_ipl, cell_size, &map);
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|
normalizeAndTruncate(map,0.2f);
|
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|
PCAFeatureMaps(map);
|
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|
size_patch[0] = map->sizeY;
|
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|
size_patch[1] = map->sizeX;
|
|
|
size_patch[2] = map->numFeatures;
|
|
|
|
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|
FeaturesMap = cv::Mat(cv::Size(map->numFeatures,map->sizeX*map->sizeY), CV_32F, map->map); // Procedure do deal with cv::Mat multichannel bug
|
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|
FeaturesMap = FeaturesMap.t();
|
|
|
freeFeatureMapObject(&map);
|
|
|
|
|
|
// Lab features //当lab = false时，用不到
|
|
|
if (_labfeatures) {
|
|
|
cv::Mat imgLab;
|
|
|
cvtColor(z, imgLab, CV_BGR2Lab);
|
|
|
unsigned char *input = (unsigned char*)(imgLab.data);
|
|
|
|
|
|
// Sparse output vector
|
|
|
cv::Mat outputLab = cv::Mat(_labCentroids.rows, size_patch[0]*size_patch[1], CV_32F, float(0));
|
|
|
|
|
|
int cntCell = 0;
|
|
|
// Iterate through each cell
|
|
|
for (int cY = cell_size; cY < z.rows-cell_size; cY+=cell_size){
|
|
|
for (int cX = cell_size; cX < z.cols-cell_size; cX+=cell_size){
|
|
|
// Iterate through each pixel of cell (cX,cY)
|
|
|
for(int y = cY; y < cY+cell_size; ++y){
|
|
|
for(int x = cX; x < cX+cell_size; ++x){
|
|
|
// Lab components for each pixel
|
|
|
float l = (float)input[(z.cols * y + x) * 3];
|
|
|
float a = (float)input[(z.cols * y + x) * 3 + 1];
|
|
|
float b = (float)input[(z.cols * y + x) * 3 + 2];
|
|
|
|
|
|
// Iterate trough each centroid
|
|
|
float minDist = FLT_MAX;
|
|
|
int minIdx = 0;
|
|
|
float *inputCentroid = (float*)(_labCentroids.data);
|
|
|
for(int k = 0; k < _labCentroids.rows; ++k){
|
|
|
float dist = ( (l - inputCentroid[3*k]) * (l - inputCentroid[3*k]) )
|
|
|
+ ( (a - inputCentroid[3*k+1]) * (a - inputCentroid[3*k+1]) )
|
|
|
+ ( (b - inputCentroid[3*k+2]) * (b - inputCentroid[3*k+2]) );
|
|
|
if(dist < minDist){
|
|
|
minDist = dist;
|
|
|
minIdx = k;
|
|
|
}
|
|
|
}
|
|
|
// Store result at output
|
|
|
outputLab.at<float>(minIdx, cntCell) += 1.0 / cell_sizeQ;
|
|
|
//((float*) outputLab.data)[minIdx * (size_patch[0]*size_patch[1]) + cntCell] += 1.0 / cell_sizeQ;
|
|
|
}
|
|
|
}
|
|
|
cntCell++;
|
|
|
}
|
|
|
}
|
|
|
// Update size_patch[2] and add features to FeaturesMap
|
|
|
size_patch[2] += _labCentroids.rows;
|
|
|
FeaturesMap.push_back(outputLab);
|
|
|
}
|
|
|
}
|
|
|
else { //raw pixel
|
|
|
FeaturesMap = RectTools::getGrayImage(z);
|
|
|
FeaturesMap -= (float) 0.5; // In Paper;
|
|
|
size_patch[0] = z.rows;
|
|
|
size_patch[1] = z.cols;
|
|
|
size_patch[2] = 1;
|
|
|
}
|
|
|
|
|
|
if (inithann) {//只有在第一帧的时候才会用到，创建/初始化 汉宁窗
|
|
|
createHanningMats();
|
|
|
}
|
|
|
FeaturesMap = hann.mul(FeaturesMap);//特征与汉宁窗相乘，起平滑作用
|
|
|
return FeaturesMap; //最后返回的是与汉宁窗相乘后的结果，，，后续还要进行与相关滤波器作相关
|
|
|
//////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
//////////////////////////////////////////////////////////////////////////////////
|
|
|
}
|
|
|
|
|
|
// Initialize Hanning window. Function called only in the first frame.
|
|
|
void KCFTracker::createHanningMats()
|
|
|
{
|
|
|
cv::Mat hann1t = cv::Mat(cv::Size(size_patch[1],1), CV_32F, cv::Scalar(0));
|
|
|
cv::Mat hann2t = cv::Mat(cv::Size(1,size_patch[0]), CV_32F, cv::Scalar(0));
|
|
|
|
|
|
for (int i = 0; i < hann1t.cols; i++)
|
|
|
hann1t.at<float > (0, i) = 0.5 * (1 - std::cos(2 * 3.14159265358979323846 * i / (hann1t.cols - 1)));
|
|
|
for (int i = 0; i < hann2t.rows; i++)
|
|
|
hann2t.at<float > (i, 0) = 0.5 * (1 - std::cos(2 * 3.14159265358979323846 * i / (hann2t.rows - 1)));
|
|
|
|
|
|
cv::Mat hann2d = hann2t * hann1t;
|
|
|
// HOG features
|
|
|
if (_hogfeatures) {
|
|
|
cv::Mat hann1d = hann2d.reshape(1,1); // Procedure do deal with cv::Mat multichannel bug
|
|
|
|
|
|
hann = cv::Mat(cv::Size(size_patch[0]*size_patch[1], size_patch[2]), CV_32F, cv::Scalar(0));
|
|
|
for (int i = 0; i < size_patch[2]; i++) {
|
|
|
for (int j = 0; j<size_patch[0]*size_patch[1]; j++) {
|
|
|
hann.at<float>(i,j) = hann1d.at<float>(0,j);
|
|
|
}
|
|
|
}
|
|
|
}
|
|
|
// Gray features
|
|
|
else {
|
|
|
hann = hann2d;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
// Calculate sub-pixel peak for one dimension
|
|
|
float KCFTracker::subPixelPeak(float left, float center, float right)
|
|
|
{
|
|
|
float divisor = 2 * center - right - left;
|
|
|
//divisor = 0.28 0.35 0.27 0.37 0.30 0.33 0.24 0.38
|
|
|
//printf("%f \n", divisor);
|
|
|
if (divisor == 0)
|
|
|
return 0;
|
|
|
|
|
|
return 0.5 * (right - left) / divisor;
|
|
|
}
|