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###############################
## PART C
###############################
CT, bgs
git@gitlab.idiv.de:sugu/camtron.git
Does
> background subtraction
> calculates centroid points of all frames in a record
###############################
###############################
#define bgs_register(x) static BGS_Register<x> register_##x(quote(x))
## >> glue together macro?
quote() adds ""
bgs_register(Tapter)
>> static BGS_Register<Tapter> register_Tapter("Tapter")
PCA?
> difference
virtual();
virtual(){ /*empty*/ }
virtual() = 0; #pure virtual
> also difference
> virtual dtor(): if pointer to base-class deletes object
> pure virtual dtor(): need to also define function body, cuz dtor is special function whis is not overriden
> interface class (needs pure virtual??)
> abc - abstract base class. can't be instantiated anymore
> abc <> interface?
// IplImage is oldskool mat and not supported anymore..
> use smartpointer like so:
auto videoAnalysis = std::make_unique<VideoAnalysis>();
videoCapture = std::make_unique<VideoCapture>();
frameProcessor = std::make_shared<FrameProcessor>();
###########
libarchive stuff
archive_read_xxx()
archive_write_xxx()
struct archive_entry
huge workaround for corrupted files
clock
random
command arg parser
???
#if CV_MAJOR_VERSION > 3 || (CV_MAJOR_VERSION == 3 && CV_SUBMINOR_VERSION >= 9)
IplImage _frame = cvIplImage(img_input);
frame = &_frame;
#else
frame = new IplImage(img_input);
#endif
#smartpointer??
#elif CV_MAJOR_VERSION >= 3
cv::Ptr<cv::BackgroundSubtractorMOG2> mog;
#endif
> What Tboy do... why did he do it?
> forked branch bgslib_qtgui_2.0.0
> Tapter
> adapter for model
> was probably copied from LBAdaptiveSOM.cpp
--> which is disabled in openCV4
Which Background Subtraction Algo to use??
median, mean, framedifference
+ simple, fast,
- not robust if light/bg changes
- slow changes ?
> adaptive bg?
fuzzy?
mixture
NAMES
Kernel?
LBSP?
Multilayer?
Sigma-Delta?
Vibe, T2F ,dp ,lb ,vumeter?
choquet, sugeno, zivkovic, pratimediod, LOBSTER
Test/Use most common >> Ground Truth
Frame Difference
WeightedMovingMean / Variance
LBAdaptiveSOM
MOG2 (Mixture Of Gaussian) MixtureOfGaussianV2.h
KNN (K Nearest Neighbour)
> fast for small fg obj
> TRY!
FuzzySugenoIntegral.h
LSBP - Local Binary Similarity Patterns - (2013)
LSBP-based GSoC ?
SuBSENSE: improved spatiotemporal LBSP + color features (2014)
Combineable with
ROI
Canny Edge Detection
> bg modeling to update BG (eg moving trees) > pixel with threshold
> optic flow (camera is also moving. ) > vectoral estimation of own movement
features
edge
canny edge detector + calc contour
> https://en.wikipedia.org/wiki/Canny_edge_detector
roi
crop
color
RGB - not so robuse by itself (sensitive to illumination, shadows, oscillations ...)
YUV
YCrCb - brightness, chroma, color
texture
robust to illumination and shadow
eg Local Binary Pattern (LBP)
https://github.com/murari023/awesome-background-subtraction (2021 new stuff!)
https://learnopencv.com/background-subtraction-with-opencv-and-bgs-libraries/
http://docs.opencv.org/2.4/doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.html
https://hackthedeveloper.com/background-subtraction-opencv-python/ #mog2 + knn in python
https://docs.opencv.org/4.3.0/d4/dd5/classcv_1_1bgsegm_1_1BackgroundSubtractorGSOC.html#details #GSOC LSBP ALGO from openCV bgsegm.hpp
https://openaccess.thecvf.com/content_cvpr_workshops_2014/W12/papers/St-Charles_Flexible_Background_Subtraction_2014_CVPR_paper.pdf
https://www.scitepress.org/Papers/2018/66296/66296.pdf #vehicle tracking latvia. 2018, BackgroundSubtractorMOG, BackgroundSubtractorMOG2 (zivkovic)
https://www-sop.inria.fr/members/Francois.Bremond/Postscript/AnhTuanAVSS14.pdf 2014
https://arxiv.org/pdf/1803.07985.pdf # visual animal tracking (2018)
https://arxiv.org/pdf/1507.06821.pdf # Multimodal Deep Learning for Robust RGB-D Object Recognition (2015)
https://towardsdatascience.com/background-removal-with-deep-learning-c4f2104b3157?gi=2ef3a5272e5d (2017 Background removal with deep learning)
https://opencv.org/courses/ #xpensive ai course
https://www.fast.ai/ #free ai course
##################
# computervision #
##################
background subtraction (bgs)
image segmentation
Semantic Segmentation (ai)
detection (feature, object)
classification (category recognition)
Challenges: Occlusion, (Sensor-)Noise, changing external conditions( lighting, Shadows, fog, reflection )
> pre-training if lack of data
> corrupt data to guarantee robust learning
######
# AI #
######
Convolutional Neural Networks (CNNs)
https://www.researchgate.net/publication/337401161_Fundamental_Concepts_of_Convolutional_Neural_Network (2020 37p)
> Kernel
> filter?
> preprocessing > Training > Parameter init > regularization > optimize
RGB-D - RGB + depth >> eg Kinect-Sensors
KNN - k-nearest neighbors algorithm
Monte Carlo tree search algorithm (alphaGo...)
RGB-D descriptor that relies on a K-Means
HMP - hierarchical matching pursuit ( hierarchical sparse-coding method; learn features from multiple channel input)
encoding depth information into three channels (HHA encoding) feature hierachy
cascade of Random Forest classifiers that are fused in a hierarchical manner ?? #Random Forest
recall - how many relevant items are retrieved?
precision - how many retrieved items are relevant?
> https://en.wikipedia.org/wiki/Recall_(information_retrieval)
?
automatically learn a fusion strategy for the recognition
task – in contrast to simply training a linear classifier on
top of features extracted from both modalities
MISRE - Multiple Input Structures with Robust Estimator
gradient-based learning > MNIST supervised image classification
MNISt - large db of hand-written digits (used for ML and ImagePRocessing)
> also see classifiers: https://en.wikipedia.org/wiki/MNIST_database#Classifiers
multimodal learning
the parameters of all layers were adapted using a fixed learning rate schedule
At test time the task of the CNN is to assign the correct class label to a previously unseen object instance.
Tasks:
Classification, clustering, regression, Summarization, Categorization, Natural language processing,
##################################
# STAT
##################################Regression
Hadamard Product
Bernoulli Distribution
Discrete Uniform Distribution
##################################
# INSTALL
##################################
sublime: install package CMAKE (syntax hilight)
apt install libarchive-dev python3-pybind11
##################################
init(img_input, img_output, img_bgmodel);
#if CV_MAJOR_VERSION > 3 || (CV_MAJOR_VERSION == 3 && CV_SUBMINOR_VERSION >= 9)
IplImage _frame = cvIplImage(img_input);
frame = &_frame;
#else
frame = new IplImage(img_input);
#endif