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You should read this paper: Histograms of Oriented Gradients for Human Detection, Navneet Dalal and Bill Triggs, CVPR 2005, PDF It explains almost everything on how to use HOG and linear SVM for pedestrian detection. It uses two dataset (MIT and INRIA) and specifies the number of samples used. Using the same parameters, you should be able to train a good pedestrian detector.

You should read this paper: Histograms of Oriented Gradients for Human Detection, Navneet Dalal and Bill Triggs, CVPR 2005, PDF It explains almost everything on how to use HOG and linear SVM for pedestrian detection. It uses two dataset (MIT and INRIA) and specifies the number of samples used. Using the same parameters, you should be able to train a good pedestrian detector.

You should read this paper: Histograms of Oriented Gradients for Human Detection, Navneet Dalal and Bill Triggs, CVPR 2005, PDF It explains almost everything on how to use HOG and linear SVM for pedestrian detection. It uses two dataset (MIT and INRIA) and specifies the number of samples used. Using the same parameters, you should be able to train a good pedestrian detector.

[ Additional information ]

HOG cascade is not supported in OpenCV 3.0, you can use HOGDescriptor

OpenCV 3.x have a sample application to train custom HOG+SVM detector train_HOG.cpp

You should read this paper: Histograms of Oriented Gradients for Human Detection, Navneet Dalal and Bill Triggs, CVPR 2005, PDF It explains almost everything on how to use HOG and linear SVM for pedestrian detection. It uses two dataset (MIT and INRIA) and specifies the number of samples used. Using the same parameters, you should be able to train a good pedestrian detector.

[ Additional information ]

HOG cascade is not supported in OpenCV 3.0, 3.0 ( see here ), instead you can use HOGDescriptor

OpenCV 3.x have a sample application to train custom HOG+SVM detector train_HOG.cpp