Problems with Traincascade: A Practical Problem

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In the past few days I've been investigating the createsamples and traincascade methods in order to generate a car cascaded classifier. Such as myself, many people find themselves not having a clue about how to get past all the errors and problems, so in this post I'll explain my procedure and reasoning as well as I can, as well as my problem.

To give a little more context, in the past I implemented myself a boosted classifier with Haar features but since the method was not cascaded it was very slow. I am, however, very well acquainted with the method behind the cascaded classification.

Problem: Car classification on images

I downloaded a dataset (set of images) that provides 550 positive windows (100w x 40h images with cars) and 500 negative windows (100w x 40h images without cars), and I intend to use this data to train the cascaded classifier.

STEP 1 - Generate the .dat files for creating samples

The createsamples application needs a .dat file with the information about the positive and negative samples. I generated two .dat files, one with positive windows and other with negative windows:

positive.dat example structure:

CarData\TrainImages\POS\pos-0.pgm 1 0 0 100 40

CarData\TrainImages\POS\pos-1.pgm 1 0 0 100 40 (and more 548 lines of this)

The number 1 indicates that there is one object in the entire image, and the next 4 integers represent the bounding box that has the object (the entire image in this example)

negative.dat example structure:

CarData\TrainImages\NEG\neg-0.pgm

CarData\TrainImages\NEG\neg-1.pgm(and more 498 lines of this)

STEP 2 - Generate .vec files

To do this I use the createsamples.exe application with the following command line:

: createsamples.exe -info path/positive.dat -vec samples.vec -bg negative.dat -w 100 -h 40 -num 550

So far so good, the samples.vec file is generated with no issue.

STEP 3 - Train the cascaded classifier

To do this I use the traincascade application with the following command line:

: traincascade.exe -data CarDetector -vec path/samples.vec -bg path/negative.dat -numStages 15 -stageType BOOST -featureType HAAR -w 100 -h 40 -bt DAB -maxDepth 2 -mode ALL

PARAMETERS EXPLAINED:

numStages(15): means that I want my classifier will have 15 decision stages.

stageType(BOOST): each stage is a boosted classifier

featureType(HAAR): use haar features for classification. HOG and LBP also available.

w(100): detection window width

h(40): detection window height

bt(DAB): means I want to use Discrete Adaboost (rather than logitboost and others)

maxDepth(2): means each weak classifier will use 3 features for classification

-mode(ALL): I think it means it will use all variety of haar features.

ERROR

When I run the command, the program crashes immediately with no error message, so I am completely clueless as to what I am doing wrong. I'm hoping that someone experienced in this matter will identify the problem with my procedure, as this is an ... (more)