A complex of TLD detailed analysis

The following is their own in the study of the paper and the analysis of these Daniel, some understanding of the code, but because of their exposure to image processing and machine vision not long, in addition to their own programming ability is weaker, so the analysis process may be a lot of mistakes, I hope you do not hesitate to correct. Moreover, because the programming many places do not understand, therefore the annotation is very messy, also haihan.

TLD.h

[CPP]  View plain copy #include  <opencv2/opencv.hpp>   #include  <tld_utils.h>    #include  <LKTracker.h>   #include  <FerNNClassifier.h>   #include  <fstream>        //bounding boxes   struct  boundingbox : public cv::rect {     boundingbox () {}      boundingbox (Cv::rect r):  cv::rect (r) {}   //inheritance requires initialization of the base class    public:      float overlap;        //Overlap with  current bounding box     int sidx;              //scale index  };     //detection  structure   struct detstruct {   &NBSP;&NBSP;&NBSP;&NBSP;STD::VECTOR&LT;INT&GT;&Nbsp;bb;       std::vector<std::vector<int> > patt;        std::vector<float> conf1;   &NBSP;&NBSP;&NBSP;&NBSP;STD:: vector<float> conf2;       std::vector<std::vector<int>  > isin;       std::vector<cv::Mat> patch;      };       //temporal structure   struct tempstruct {        std::vector<std::vector<int> > patt;       std::vector<float> conf;     };      struct ocomparator{  //Compare the coincidence degree      ocomparator (const std::vector< Boundingbox>& _grid): Grid (_grid) {}     std::vector<boundingbox> grid ;  &nbsP   bool operator () (INT&NBSP;IDX1,INT&NBSP;IDX2) {       return  grid[idx1].overlap > grid[idx2].overlap;     }  };       struct ccomparator{  //compares the two degrees of certainty.      ccomparator (const std::vector<float>& _conf): Conf (_conf) {}      std::vector<float> conf;     bool operator () (int  IDX1,INT&NBSP;IDX2) {       return conf[idx1]> conf[idx2];      }  };         class tld{