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tracking: rework tracking API
- simplify Tracker API - fix perf tests (don't measure video decoding)
This commit is contained in:
Alexander Alekhin
271
modules/tracking/include/opencv2/tracking.hpp
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271
modules/tracking/include/opencv2/tracking.hpp
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef OPENCV_CONTRIB_TRACKING_HPP
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#define OPENCV_CONTRIB_TRACKING_HPP
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#include "opencv2/core.hpp"
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namespace cv {
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#ifndef CV_DOXYGEN
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inline namespace tracking {
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#endif
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/** @defgroup tracking Tracking API
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@{
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@defgroup tracking_detail Tracking API implementation details
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@defgroup tracking_legacy Legacy Tracking API
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@}
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*/
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/** @addtogroup tracking
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@{
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Tracking is an important issue for many computer vision applications in real world scenario.
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The development in this area is very fragmented and this API is an interface useful for plug several algorithms and compare them.
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*/
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/** @brief Base abstract class for the long-term tracker
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*/
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class CV_EXPORTS_W Tracker
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{
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protected:
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Tracker();
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public:
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virtual ~Tracker();
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/** @brief Initialize the tracker with a known bounding box that surrounded the target
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@param image The initial frame
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@param boundingBox The initial bounding box
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*/
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CV_WRAP virtual
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void init(InputArray image, const Rect& boundingBox) = 0;
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/** @brief Update the tracker, find the new most likely bounding box for the target
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@param image The current frame
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@param boundingBox The bounding box that represent the new target location, if true was returned, not
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modified otherwise
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@return True means that target was located and false means that tracker cannot locate target in
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current frame. Note, that latter *does not* imply that tracker has failed, maybe target is indeed
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missing from the frame (say, out of sight)
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*/
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CV_WRAP virtual
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bool update(InputArray image, CV_OUT Rect& boundingBox) = 0;
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};
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/** @brief the CSRT tracker
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The implementation is based on @cite Lukezic_IJCV2018 Discriminative Correlation Filter with Channel and Spatial Reliability
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*/
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class CV_EXPORTS_W TrackerCSRT : public Tracker
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{
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protected:
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TrackerCSRT(); // use ::create()
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public:
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virtual ~TrackerCSRT() CV_OVERRIDE;
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struct CV_EXPORTS_W_SIMPLE Params
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{
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CV_WRAP Params();
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CV_PROP_RW bool use_hog;
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CV_PROP_RW bool use_color_names;
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CV_PROP_RW bool use_gray;
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CV_PROP_RW bool use_rgb;
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CV_PROP_RW bool use_channel_weights;
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CV_PROP_RW bool use_segmentation;
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CV_PROP_RW std::string window_function; //!< Window function: "hann", "cheb", "kaiser"
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CV_PROP_RW float kaiser_alpha;
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CV_PROP_RW float cheb_attenuation;
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CV_PROP_RW float template_size;
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CV_PROP_RW float gsl_sigma;
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CV_PROP_RW float hog_orientations;
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CV_PROP_RW float hog_clip;
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CV_PROP_RW float padding;
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CV_PROP_RW float filter_lr;
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CV_PROP_RW float weights_lr;
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CV_PROP_RW int num_hog_channels_used;
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CV_PROP_RW int admm_iterations;
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CV_PROP_RW int histogram_bins;
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CV_PROP_RW float histogram_lr;
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CV_PROP_RW int background_ratio;
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CV_PROP_RW int number_of_scales;
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CV_PROP_RW float scale_sigma_factor;
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CV_PROP_RW float scale_model_max_area;
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CV_PROP_RW float scale_lr;
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CV_PROP_RW float scale_step;
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CV_PROP_RW float psr_threshold; //!< we lost the target, if the psr is lower than this.
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};
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/** @brief Create CSRT tracker instance
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@param parameters CSRT parameters TrackerCSRT::Params
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*/
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static CV_WRAP
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Ptr<TrackerCSRT> create(const TrackerCSRT::Params ¶meters = TrackerCSRT::Params());
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//void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
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//bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
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CV_WRAP virtual void setInitialMask(InputArray mask) = 0;
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};
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/** @brief the KCF (Kernelized Correlation Filter) tracker
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* KCF is a novel tracking framework that utilizes properties of circulant matrix to enhance the processing speed.
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* This tracking method is an implementation of @cite KCF_ECCV which is extended to KCF with color-names features (@cite KCF_CN).
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* The original paper of KCF is available at <http://www.robots.ox.ac.uk/~joao/publications/henriques_tpami2015.pdf>
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* as well as the matlab implementation. For more information about KCF with color-names features, please refer to
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* <http://www.cvl.isy.liu.se/research/objrec/visualtracking/colvistrack/index.html>.
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*/
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class CV_EXPORTS_W TrackerKCF : public Tracker
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{
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protected:
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TrackerKCF(); // use ::create()
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public:
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virtual ~TrackerKCF() CV_OVERRIDE;
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/**
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* \brief Feature type to be used in the tracking grayscale, colornames, compressed color-names
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* The modes available now:
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- "GRAY" -- Use grayscale values as the feature
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- "CN" -- Color-names feature
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*/
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enum MODE {
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GRAY = (1 << 0),
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CN = (1 << 1),
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CUSTOM = (1 << 2)
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};
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struct CV_EXPORTS_W_SIMPLE Params
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{
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CV_WRAP Params();
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CV_PROP_RW float detect_thresh; //!< detection confidence threshold
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CV_PROP_RW float sigma; //!< gaussian kernel bandwidth
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CV_PROP_RW float lambda; //!< regularization
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CV_PROP_RW float interp_factor; //!< linear interpolation factor for adaptation
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CV_PROP_RW float output_sigma_factor; //!< spatial bandwidth (proportional to target)
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CV_PROP_RW float pca_learning_rate; //!< compression learning rate
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CV_PROP_RW bool resize; //!< activate the resize feature to improve the processing speed
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CV_PROP_RW bool split_coeff; //!< split the training coefficients into two matrices
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CV_PROP_RW bool wrap_kernel; //!< wrap around the kernel values
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CV_PROP_RW bool compress_feature; //!< activate the pca method to compress the features
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CV_PROP_RW int max_patch_size; //!< threshold for the ROI size
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CV_PROP_RW int compressed_size; //!< feature size after compression
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CV_PROP_RW int desc_pca; //!< compressed descriptors of TrackerKCF::MODE
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CV_PROP_RW int desc_npca; //!< non-compressed descriptors of TrackerKCF::MODE
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};
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/** @brief Create KCF tracker instance
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@param parameters KCF parameters TrackerKCF::Params
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*/
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static CV_WRAP
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Ptr<TrackerKCF> create(const TrackerKCF::Params ¶meters = TrackerKCF::Params());
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//void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
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//bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
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// FIXIT use interface
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typedef void (*FeatureExtractorCallbackFN)(const Mat, const Rect, Mat&);
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virtual void setFeatureExtractor(FeatureExtractorCallbackFN callback, bool pca_func = false) = 0;
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};
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/** @brief The MIL algorithm trains a classifier in an online manner to separate the object from the
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background.
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Multiple Instance Learning avoids the drift problem for a robust tracking. The implementation is
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based on @cite MIL .
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Original code can be found here <http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml>
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*/
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class CV_EXPORTS_W TrackerMIL : public Tracker
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{
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protected:
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TrackerMIL(); // use ::create()
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public:
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virtual ~TrackerMIL() CV_OVERRIDE;
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struct CV_EXPORTS_W_SIMPLE Params
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{
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CV_WRAP Params();
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//parameters for sampler
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CV_PROP_RW float samplerInitInRadius; //!< radius for gathering positive instances during init
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CV_PROP_RW int samplerInitMaxNegNum; //!< # negative samples to use during init
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CV_PROP_RW float samplerSearchWinSize; //!< size of search window
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CV_PROP_RW float samplerTrackInRadius; //!< radius for gathering positive instances during tracking
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CV_PROP_RW int samplerTrackMaxPosNum; //!< # positive samples to use during tracking
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CV_PROP_RW int samplerTrackMaxNegNum; //!< # negative samples to use during tracking
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CV_PROP_RW int featureSetNumFeatures; //!< # features
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};
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/** @brief Create MIL tracker instance
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* @param parameters MIL parameters TrackerMIL::Params
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*/
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static CV_WRAP
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Ptr<TrackerMIL> create(const TrackerMIL::Params ¶meters = TrackerMIL::Params());
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//void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
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//bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
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};
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/** @brief the GOTURN (Generic Object Tracking Using Regression Networks) tracker
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*
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* GOTURN (@cite GOTURN) is kind of trackers based on Convolutional Neural Networks (CNN). While taking all advantages of CNN trackers,
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* GOTURN is much faster due to offline training without online fine-tuning nature.
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* GOTURN tracker addresses the problem of single target tracking: given a bounding box label of an object in the first frame of the video,
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* we track that object through the rest of the video. NOTE: Current method of GOTURN does not handle occlusions; however, it is fairly
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* robust to viewpoint changes, lighting changes, and deformations.
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* Inputs of GOTURN are two RGB patches representing Target and Search patches resized to 227x227.
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* Outputs of GOTURN are predicted bounding box coordinates, relative to Search patch coordinate system, in format X1,Y1,X2,Y2.
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* Original paper is here: <http://davheld.github.io/GOTURN/GOTURN.pdf>
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* As long as original authors implementation: <https://github.com/davheld/GOTURN#train-the-tracker>
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* Implementation of training algorithm is placed in separately here due to 3d-party dependencies:
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* <https://github.com/Auron-X/GOTURN_Training_Toolkit>
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* GOTURN architecture goturn.prototxt and trained model goturn.caffemodel are accessible on opencv_extra GitHub repository.
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*/
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class CV_EXPORTS_W TrackerGOTURN : public Tracker
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{
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protected:
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TrackerGOTURN(); // use ::create()
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public:
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virtual ~TrackerGOTURN() CV_OVERRIDE;
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struct CV_EXPORTS_W_SIMPLE Params
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{
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CV_WRAP Params();
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CV_PROP_RW std::string modelTxt;
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CV_PROP_RW std::string modelBin;
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};
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/** @brief Constructor
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@param parameters GOTURN parameters TrackerGOTURN::Params
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*/
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static CV_WRAP
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Ptr<TrackerGOTURN> create(const TrackerGOTURN::Params& parameters = TrackerGOTURN::Params());
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//void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
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//bool update(InputArray image, CV_OUT Rect& boundingBox) CV_OVERRIDE;
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};
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//! @}
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#ifndef CV_DOXYGEN
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}
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#endif
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} // namespace
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#endif // OPENCV_CONTRIB_TRACKING_HPP
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