【算法基础】卡尔曼滤波KF

kalman filter

 

KCF

尺度变化是跟踪中比较基本和常见的问题，前面介绍的三个算法都没有尺度更新，如果目标缩小，滤波器就会学习到大量背景信息，如果目标扩大，滤波器就跟着目标局部纹理走了，这两种情况都很可能出现非预期的结果，导致漂移和失败。

https://blog.csdn.net/wfei101/article/details/79673275

https://www.cnblogs.com/YiXiaoZhou/p/5925019.html

http://www.robots.ox.ac.uk/~joao/circulant/

https://www.cnblogs.com/fx-blog/p/8213704.html

https://blog.csdn.net/crazyice521/article/category/6282914

 https://blog.csdn.net/denghecsdn/article/details/78418748

https://elbauldelprogramador.com/en/how-to-compile-opencv3-nonfree-part-from-source/

 https://github.com/joaofaro/KCFcpp

  struct CV_EXPORTS Params
  {
    /**  
    * \brief Constructor
    */
    Params();

    /**  
    * \brief Read parameters from a file
    */
    void read(const FileNode& /*fn*/);

    /**  
    * \brief Write parameters to a file
    */
    void write(FileStorage& /*fs*/) const;

    float detect_thresh;         //!<  detection confidence threshold
    float sigma;                 //!<  gaussian kernel bandwidth
    float lambda;                //!<  regularization
    float interp_factor;         //!<  linear interpolation factor for adaptation
    float output_sigma_factor;   //!<  spatial bandwidth (proportional to target)
    float pca_learning_rate;     //!<  compression learning rate
    bool resize;                  //!<  activate the resize feature to improve the processing speed
    bool split_coeff;             //!<  split the training coefficients into two matrices
    bool wrap_kernel;             //!<  wrap around the kernel values
    bool compress_feature;        //!<  activate the pca method to compress the features
    int max_patch_size;           //!<  threshold for the ROI size
    int compressed_size;          //!<  feature size after compression
    int desc_pca;        //!<  compressed descriptors of TrackerKCF::MODE
    int desc_npca;       //!<  non-compressed descriptors of TrackerKCF::MODE
  };

  /** @brief Constructor
  @param parameters KCF parameters TrackerKCF::Params
  */
  static Ptr<TrackerKCF> create(const TrackerKCF::Params &parameters);

 

dlib中自带的correlation_tracker类

http://dlib.net/python/index.html#dlib.correlation_tracker

Danelljan, Martin, et al. ‘Accurate scale estimation for robust visual tracking.’ Proceedings of the British Machine Vision Conference BMVC. 2014.

参考

1.

https://www.cnblogs.com/xmphoenix/p/3634536.html

完