Multiple parameter multiple model training with cascade classifiers, error on system() call

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My setup: Linux Ubuntu 14.04 64 bit with OpenCV 2.4 branch compiled and installed.

Goal: to train multiple cascade classifier models with different parameters by executing a single command, so that I can fire it up at the end of my day of work and evaluate the results the day after when heading back.

Approach:

• Using the system() call in C++ I subsequently want to invoke a opencv_traincascade command
• The following command works from every single location on my system when manually passed to a terminal interface

code sample

opencv_traincascade -data /data/datasets/candy_model/working_model/cascade/0/ -vec /data/datasets/candy_model/working_model/output20150305.vec -bg /data/datasets/candy_model/working_model/negatives.txt -numPos 25 -numNeg 250 -numStages 20 -w 59 -h 23 -precalcValBufSize 2048 -precalcIdxBufSize 2048

• If you want this to work decently, you need to apply a small fix to the traincascade algorithm described in this PR, to allow the use of absolute paths for reading your data.
• Now running the same command in my C++ OpenCV based project, with the command system(buffer.str().c_str() ); and the buffer being filled with the exact same content as the manual command (being checked by 5 collegues at work to avoid typos) raises the problem that the training cannot be performed as seen below

generated output

cascadeDirName: /data/datasets/candy_model/working_model/cascade/0/
vecFileName: /data/datasets/candy_model/working_model/output20150305.vec
bgFileName: /data/datasets/candy_model/working_model/negatives.txt
numPos: 25
numNeg: 250
numStages: 20
precalcValBufSize[Mb] : 2048
precalcIdxBufSize[Mb] : 2048
stageType: BOOST
featureType: HAAR
sampleWidth: 59
sampleHeight: 23
boostType: GAB
minHitRate: 0.995
maxFalseAlarmRate: 0.5
weightTrimRate: 0.95
maxDepth: 1
maxWeakCount: 100
mode: BASIC

===== TRAINING 0-stage =====
<BEGIN
Train dataset for temp stage can not be filled. Branch training terminated.
Cascade classifier can't be trained. Check the used training parameters.