OpenCV  3.4.9
Open Source Computer Vision
Customizing the CN Tracker

Goal

In this tutorial you will learn how to

This document contains tutorial for the cv::TrackerKCF.

Source Code

1 #include <opencv2/core/utility.hpp>
2 #include <opencv2/tracking.hpp>
3 #include <opencv2/videoio.hpp>
4 #include <opencv2/highgui.hpp>
5 #include <iostream>
6 #include <cstring>
7 #include "samples_utility.hpp"
8 
9 using namespace std;
10 using namespace cv;
11 
12 // prototype of the functino for feature extractor
13 void sobelExtractor(const Mat img, const Rect roi, Mat& feat);
14 
15 int main( int argc, char** argv ){
16  // show help
17  if(argc<2){
18  cout<<
19  " Usage: tracker <video_name>\n"
20  " examples:\n"
21  " example_tracking_kcf Bolt/img/%04d.jpg\n"
22  " example_tracking_kcf faceocc2.webm\n"
23  << endl;
24  return 0;
25  }
26 
27  // declares all required variables
28  Rect2d roi;
29  Mat frame;
30 
32  TrackerKCF::Params param;
33  param.desc_pca = TrackerKCF::GRAY | TrackerKCF::CN;
34  param.desc_npca = 0;
35  param.compress_feature = true;
36  param.compressed_size = 2;
38 
39  // create a tracker object
41  Ptr<TrackerKCF> tracker = TrackerKCF::create(param);
43 
45  tracker->setFeatureExtractor(sobelExtractor);
47 
48  // set input video
49  std::string video = argv[1];
50  VideoCapture cap(video);
51 
52  // get bounding box
53  cap >> frame;
54  roi=selectROI("tracker",frame);
55 
56  //quit if ROI was not selected
57  if(roi.width==0 || roi.height==0)
58  return 0;
59 
60  // initialize the tracker
61  tracker->init(frame,roi);
62 
63  // perform the tracking process
64  printf("Start the tracking process, press ESC to quit.\n");
65  for ( ;; ){
66  // get frame from the video
67  cap >> frame;
68 
69  // stop the program if no more images
70  if(frame.rows==0 || frame.cols==0)
71  break;
72 
73  // update the tracking result
74  tracker->update(frame,roi);
75 
76  // draw the tracked object
77  rectangle( frame, roi, Scalar( 255, 0, 0 ), 2, 1 );
78 
79  // show image with the tracked object
80  imshow("tracker",frame);
81 
82  //quit on ESC button
83  if(waitKey(1)==27)break;
84  }
85 
86  return 0;
87 }
88 
89 void sobelExtractor(const Mat img, const Rect roi, Mat& feat){
90  Mat sobel[2];
91  Mat patch;
92  Rect region=roi;
93 
95  // extract patch inside the image
96  if(roi.x<0){region.x=0;region.width+=roi.x;}
97  if(roi.y<0){region.y=0;region.height+=roi.y;}
98  if(roi.x+roi.width>img.cols)region.width=img.cols-roi.x;
99  if(roi.y+roi.height>img.rows)region.height=img.rows-roi.y;
100  if(region.width>img.cols)region.width=img.cols;
101  if(region.height>img.rows)region.height=img.rows;
103 
104  patch=img(region).clone();
105  cvtColor(patch,patch, COLOR_BGR2GRAY);
106 
108  // add some padding to compensate when the patch is outside image border
109  int addTop,addBottom, addLeft, addRight;
110  addTop=region.y-roi.y;
111  addBottom=(roi.height+roi.y>img.rows?roi.height+roi.y-img.rows:0);
112  addLeft=region.x-roi.x;
113  addRight=(roi.width+roi.x>img.cols?roi.width+roi.x-img.cols:0);
114 
115  copyMakeBorder(patch,patch,addTop,addBottom,addLeft,addRight,BORDER_REPLICATE);
117 
119  Sobel(patch, sobel[0], CV_32F,1,0,1);
120  Sobel(patch, sobel[1], CV_32F,0,1,1);
121 
122  merge(sobel,2,feat);
124 
126  feat.convertTo(feat,CV_64F);
127  feat=feat/255.0-0.5; // normalize to range -0.5 .. 0.5
129 }
cv::Scalar
Scalar_< double > Scalar
Definition: types.hpp:657
cv::COLOR_BGR2GRAY
convert between RGB/BGR and grayscale, color conversions
Definition: imgproc.hpp:544
cv::BORDER_REPLICATE
aaaaaa|abcdefgh|hhhhhhh
Definition: base.hpp:270
cv::Mat::convertTo
void convertTo(OutputArray m, int rtype, double alpha=1, double beta=0) const
Converts an array to another data type with optional scaling.
cv::Rect_::x
_Tp x
x coordinate of the top-left corner
Definition: types.hpp:447
cv::Mat::rows
int rows
the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions ...
Definition: mat.hpp:2114
cv::TrackerKCF::Params::desc_pca
int desc_pca
compressed descriptors of TrackerKCF::MODE
Definition: tracker.hpp:1256
std
STL namespace.
cv::rectangle
void rectangle(InputOutputArray img, Point pt1, Point pt2, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
Draws a simple, thick, or filled up-right rectangle.
cv::imshow
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.
cv
Definition: affine.hpp:51
cv::selectROI
Rect selectROI(const String &windowName, InputArray img, bool showCrosshair=true, bool fromCenter=false)
Selects ROI on the given image. Function creates a window and allows user to select a ROI using mouse...
cv::VideoCapture
Class for video capturing from video files, image sequences or cameras.
Definition: videoio.hpp:614
cv::cvtColor
void cvtColor(InputArray src, OutputArray dst, int code, int dstCn=0)
Converts an image from one color space to another.
cv::Rect_::y
_Tp y
y coordinate of the top-left corner
Definition: types.hpp:448
cv::TrackerKCF::Params::desc_npca
int desc_npca
non-compressed descriptors of TrackerKCF::MODE
Definition: tracker.hpp:1257
cv::Sobel
void Sobel(InputArray src, OutputArray dst, int ddepth, int dx, int dy, int ksize=3, double scale=1, double delta=0, int borderType=BORDER_DEFAULT)
Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator...
cv::copyMakeBorder
void copyMakeBorder(InputArray src, OutputArray dst, int top, int bottom, int left, int right, int borderType, const Scalar &value=Scalar())
Forms a border around an image.
CV_32F
#define CV_32F
Definition: interface.h:76
cv::Rect_::height
_Tp height
height of the rectangle
Definition: types.hpp:450
cv::Mat::cols
int cols
Definition: mat.hpp:2114
cv::TrackerKCF::Params::compress_feature
bool compress_feature
activate the pca method to compress the features
Definition: tracker.hpp:1253
CV_64F
#define CV_64F
Definition: interface.h:77
cv::Rect_
Template class for 2D rectangles.
Definition: types.hpp:416
cv::Ptr
Template class for smart pointers with shared ownership.
Definition: cvstd.hpp:261
cv::merge
void merge(const Mat *mv, size_t count, OutputArray dst)
Creates one multi-channel array out of several single-channel ones.
cv::Rect_::width
_Tp width
width of the rectangle
Definition: types.hpp:449
cv::TrackerKCF::Params::compressed_size
int compressed_size
feature size after compression
Definition: tracker.hpp:1255
cv::Tracker::init
bool init(InputArray image, const Rect2d &boundingBox)
Initialize the tracker with a known bounding box that surrounded the target.
cv::Mat::clone
Mat clone() const CV_NODISCARD
Creates a full copy of the array and the underlying data.
cv::Tracker::update
bool update(InputArray image, Rect2d &boundingBox)
Update the tracker, find the new most likely bounding box for the target.
cv::Mat
n-dimensional dense array class
Definition: mat.hpp:804
cv::TrackerKCF::setFeatureExtractor
virtual void setFeatureExtractor(void(*)(const Mat, const Rect, Mat &), bool pca_func=false)=0
cv::TrackerKCF::Params
Definition: tracker.hpp:1227
cv::waitKey
int waitKey(int delay=0)
Waits for a pressed key.

Explanation

This part explains how to set custom parameters and use your own feature-extractor function for the CN tracker. If you need a more detailed information to use cv::Tracker, please refer to Introduction to OpenCV Tracker.

  1. Set Custom Parameters

    TrackerKCF::Params param;
    param.desc_pca = TrackerKCF::GRAY | TrackerKCF::CN;
    param.desc_npca = 0;
    param.compress_feature = true;
    param.compressed_size = 2;

    To set custom paramters, an object should be created. Each tracker algorithm has their own parameter format. So, in this case we should use parameter from cv::TrackerKCF since we are interested in modifying the parameter of this tracker algorithm.

    There are several parameters that can be configured as explained in cv::TrackerKCF::Params. For this tutorial, we focussed on the feature extractor functions.

    Several feature types can be used in cv::TrackerKCF. In this case, the grayscale value (1 dimension) and color-names features (10 dimension), will be merged as 11 dimension feature and then compressed into 2 dimension as specified in the code.

    If you want to use another type of pre-defined feature-extractor function, you can check in cv::TrackerKCF::MODE. We will leave the non-compressed feature as 0 since we want to use a customized function.

  2. Using a custom function

    You can define your own feature-extractor function for the CN tracker. However, you need to take care about several things:

    • The extracted feature should have the same size as the size of the given bounding box (width and height). For the number of channels you can check the limitation in cv::Mat.
    • You can only use features that can be compared using Euclidean distance. Features like local binary pattern (LBP) may not be suitable since it should be compared using Hamming distance.

    Since the size of the extracted feature should be in the same size with the given bounding box, we need to take care whenever the given bounding box is partially out of range. In this case, we can copy part of image contained in the bounding box as shown in the snippet below.

    // extract patch inside the image
    if(roi.x<0){region.x=0;region.width+=roi.x;}
    if(roi.y<0){region.y=0;region.height+=roi.y;}
    if(roi.x+roi.width>img.cols)region.width=img.cols-roi.x;
    if(roi.y+roi.height>img.rows)region.height=img.rows-roi.y;
    if(region.width>img.cols)region.width=img.cols;
    if(region.height>img.rows)region.height=img.rows;

    Whenever the copied image is smaller than the given bounding box, padding should be given to the sides where the bounding box is partially out of frame.

    // add some padding to compensate when the patch is outside image border
    int addTop,addBottom, addLeft, addRight;
    addTop=region.y-roi.y;
    addBottom=(roi.height+roi.y>img.rows?roi.height+roi.y-img.rows:0);
    addLeft=region.x-roi.x;
    addRight=(roi.width+roi.x>img.cols?roi.width+roi.x-img.cols:0);
    copyMakeBorder(patch,patch,addTop,addBottom,addLeft,addRight,BORDER_REPLICATE);

  3. Defining the feature

    In this tutorial, the extracted feature is response of the Sobel filter in x and y direction. Those Sobel filter responses are concatenated, resulting a feature with 2 channels.

    Sobel(patch, sobel[0], CV_32F,1,0,1);
    Sobel(patch, sobel[1], CV_32F,0,1,1);
    merge(sobel,2,feat);

  4. Post processing

    Make sure to convert the feature into cv::CV_64F data format and normalize its value with range -0.5 to 0.5

    feat.convertTo(feat,CV_64F);
    feat=feat/255.0-0.5; // normalize to range -0.5 .. 0.5
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