263 lines
9.1 KiB
ReStructuredText
263 lines
9.1 KiB
ReStructuredText
Latent SVM
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===============================================================
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Discriminatively Trained Part Based Models for Object Detection
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---------------------------------------------------------------
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The object detector described below has been initially proposed by
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P.F. Felzenszwalb in [Felzenszwalb2010]_. It is based on a
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Dalal-Triggs detector that uses a single filter on histogram of
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oriented gradients (HOG) features to represent an object category.
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This detector uses a sliding window approach, where a filter is
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applied at all positions and scales of an image. The first
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innovation is enriching the Dalal-Triggs model using a
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star-structured part-based model defined by a "root" filter
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(analogous to the Dalal-Triggs filter) plus a set of parts filters
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and associated deformation models. The score of one of star models
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at a particular position and scale within an image is the score of
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the root filter at the given location plus the sum over parts of the
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maximum, over placements of that part, of the part filter score on
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its location minus a deformation cost easuring the deviation of the
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part from its ideal location relative to the root. Both root and
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part filter scores are defined by the dot product between a filter
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(a set of weights) and a subwindow of a feature pyramid computed
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from the input image. Another improvement is a representation of the
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class of models by a mixture of star models. The score of a mixture
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model at a particular position and scale is the maximum over
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components, of the score of that component model at the given
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location.
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In OpenCV there are C implementation of Latent SVM and C++ wrapper of it.
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C version is the structure :ocv:struct:`CvObjectDetection` and a set of functions
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working with this structure (see :ocv:func:`cvLoadLatentSvmDetector`,
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:ocv:func:`cvReleaseLatentSvmDetector`, :ocv:func:`cvLatentSvmDetectObjects`).
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C++ version is the class :ocv:class:`LatentSvmDetector` and has slightly different
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functionality in contrast with C version - it supports loading and detection
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of several models.
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There are two examples of Latent SVM usage: ``samples/c/latentsvmdetect.cpp``
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and ``samples/cpp/latentsvm_multidetect.cpp``.
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.. highlight:: c
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CvLSVMFilterPosition
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--------------------
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.. ocv:struct:: CvLSVMFilterPosition
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Structure describes the position of the filter in the feature pyramid.
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.. ocv:member:: unsigned int l
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level in the feature pyramid
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.. ocv:member:: unsigned int x
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x-coordinate in level l
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.. ocv:member:: unsigned int y
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y-coordinate in level l
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CvLSVMFilterObject
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------------------
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.. ocv:struct:: CvLSVMFilterObject
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Description of the filter, which corresponds to the part of the object.
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.. ocv:member:: CvLSVMFilterPosition V
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ideal (penalty = 0) position of the partial filter
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from the root filter position (V_i in the paper)
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.. ocv:member:: float fineFunction[4]
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vector describes penalty function (d_i in the paper)
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pf[0] * x + pf[1] * y + pf[2] * x^2 + pf[3] * y^2
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.. ocv:member:: int sizeX
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.. ocv:member:: int sizeY
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Rectangular map (sizeX x sizeY),
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every cell stores feature vector (dimension = p)
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.. ocv:member:: int numFeatures
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number of features
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.. ocv:member:: float *H
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matrix of feature vectors to set and get
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feature vectors (i,j) used formula H[(j * sizeX + i) * p + k],
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where k - component of feature vector in cell (i, j)
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CvLatentSvmDetector
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-------------------
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.. ocv:struct:: CvLatentSvmDetector
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Structure contains internal representation of trained Latent SVM detector.
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.. ocv:member:: int num_filters
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total number of filters (root plus part) in model
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.. ocv:member:: int num_components
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number of components in model
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.. ocv:member:: int* num_part_filters
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array containing number of part filters for each component
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.. ocv:member:: CvLSVMFilterObject** filters
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root and part filters for all model components
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.. ocv:member:: float* b
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biases for all model components
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.. ocv:member:: float score_threshold
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confidence level threshold
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CvObjectDetection
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-----------------
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.. ocv:struct:: CvObjectDetection
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Structure contains the bounding box and confidence level for detected object.
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.. ocv:member:: CvRect rect
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bounding box for a detected object
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.. ocv:member:: float score
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confidence level
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cvLoadLatentSvmDetector
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-----------------------
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Loads trained detector from a file.
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.. ocv:function:: CvLatentSvmDetector* cvLoadLatentSvmDetector(const char* filename)
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:param filename: Name of the file containing the description of a trained detector
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cvReleaseLatentSvmDetector
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--------------------------
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Release memory allocated for CvLatentSvmDetector structure.
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.. ocv:function:: void cvReleaseLatentSvmDetector(CvLatentSvmDetector** detector)
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:param detector: CvLatentSvmDetector structure to be released
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cvLatentSvmDetectObjects
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------------------------
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Find rectangular regions in the given image that are likely to contain objects
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and corresponding confidence levels.
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.. ocv:function:: CvSeq* cvLatentSvmDetectObjects( IplImage* image, CvLatentSvmDetector* detector, CvMemStorage* storage, float overlap_threshold=0.5f, int numThreads=-1 )
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:param image: image
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:param detector: LatentSVM detector in internal representation
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:param storage: Memory storage to store the resultant sequence of the object candidate rectangles
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:param overlap_threshold: Threshold for the non-maximum suppression algorithm
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:param numThreads: Number of threads used in parallel version of the algorithm
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.. highlight:: cpp
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LatentSvmDetector
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-----------------
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.. ocv:class:: LatentSvmDetector
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This is a C++ wrapping class of Latent SVM. It contains internal representation of several
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trained Latent SVM detectors (models) and a set of methods to load the detectors and detect objects
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using them.
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LatentSvmDetector::ObjectDetection
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----------------------------------
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.. ocv:struct:: LatentSvmDetector::ObjectDetection
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Structure contains the detection information.
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.. ocv:member:: Rect rect
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bounding box for a detected object
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.. ocv:member:: float score
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confidence level
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.. ocv:member:: int classID
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class (model or detector) ID that detect an object
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LatentSvmDetector::LatentSvmDetector
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------------------------------------
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Two types of constructors.
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.. ocv:function:: LatentSvmDetector::LatentSvmDetector()
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.. ocv:function:: LatentSvmDetector::LatentSvmDetector(const vector<string>& filenames, const vector<string>& classNames=vector<string>())
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:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
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:param classNames: A set of trained models names. If it's empty then the name of each model will be constructed from the name of file containing the model. E.g. the model stored in "/home/user/cat.xml" will get the name "cat".
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LatentSvmDetector::~LatentSvmDetector
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-------------------------------------
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Destructor.
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.. ocv:function:: LatentSvmDetector::~LatentSvmDetector()
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LatentSvmDetector::~clear
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-------------------------
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Clear all trained models and their names stored in an class object.
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.. ocv:function:: void LatentSvmDetector::clear()
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LatentSvmDetector::load
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-----------------------
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Load the trained models from given ``.xml`` files and return ``true`` if at least one model was loaded.
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.. ocv:function:: bool LatentSvmDetector::load( const vector<string>& filenames, const vector<string>& classNames=vector<string>() )
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:param filenames: A set of filenames storing the trained detectors (models). Each file contains one model. See examples of such files here /opencv_extra/testdata/cv/latentsvmdetector/models_VOC2007/.
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:param classNames: A set of trained models names. If it's empty then the name of each model will be constructed from the name of file containing the model. E.g. the model stored in "/home/user/cat.xml" will get the name "cat".
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LatentSvmDetector::detect
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-------------------------
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Find rectangular regions in the given image that are likely to contain objects of loaded classes (models)
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and corresponding confidence levels.
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.. ocv:function:: void LatentSvmDetector::detect( const Mat& image, vector<ObjectDetection>& objectDetections, float overlapThreshold=0.5f, int numThreads=-1 )
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:param image: An image.
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:param objectDetections: The detections: rectangulars, scores and class IDs.
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:param overlapThreshold: Threshold for the non-maximum suppression algorithm.
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:param numThreads: Number of threads used in parallel version of the algorithm.
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LatentSvmDetector::getClassNames
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--------------------------------
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Return the class (model) names that were passed in constructor or method ``load`` or extracted from models filenames in those methods.
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.. ocv:function:: const vector<string>& LatentSvmDetector::getClassNames() const
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LatentSvmDetector::getClassCount
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--------------------------------
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Return a count of loaded models (classes).
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.. ocv:function:: size_t LatentSvmDetector::getClassCount() const
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.. [Felzenszwalb2010] Felzenszwalb, P. F. and Girshick, R. B. and McAllester, D. and Ramanan, D. *Object Detection with Discriminatively Trained Part Based Models*. PAMI, vol. 32, no. 9, pp. 1627-1645, September 2010
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