Description: Read positive and negative training sample image files specified directories
Calculate their HOG features and keep track of their classes (pos, neg)
Save the feature map (vector of vectors/matrix) to file system
Read in and pass the features and their classes to a machine learning algorithm, e.g. SVMlight
Train the machine learning algorithm using the specified parameters
Use the calculated support vectors and SVM model to calculate a single detecting descriptor vector
Dry-run the newly trained custom HOG descriptor against training set and against camera images, if available
To Search:
File list (Check if you may need any files):
trainHOG-master
...............\.gitignore
...............\ApacheLicense2.txt
...............\Makefile
...............\Readme.md
...............\genfiles
...............\........\.gitignore
...............\libsvm
...............\......\libsvm.h
...............\main.cpp
...............\nbproject
...............\.........\Makefile-Debug.mk
...............\.........\Makefile-Release.mk
...............\.........\Makefile-impl.mk
...............\.........\Makefile-variables.mk
...............\.........\Package-Debug.bash
...............\.........\Package-Release.bash
...............\.........\configurations.xml
...............\.........\private
...............\.........\.......\Makefile-variables.mk
...............\.........\.......\configurations.xml
...............\.........\project.xml
...............\neg
...............\...\.gitignore
...............\pos
...............\...\.gitignore
...............\sidefiles
...............\.........\IDEView.png
...............\.........\fileStructureSVMlight.png
...............\svmlight
...............\........\svmlight.h
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