【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 ¶meters);
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
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