cascaded Classifier Training-----OpenCV

Keywords: cascade classifier, Opencv_traincascade

The following is a brief description of the operation process:

1. Prepare positive and negative samples: neg, pos
2. Positive and negative sample path generation: Dir/a/b>path.txt//path:pos or neg
3. Positive sample Training Set generation: Opencv_createsamples.exe-info Pos\pos.txt-vec pos\pos.vec-num 799-w 24-h Pause
4. Sample training: Opencv_traincascade.exe-data Data-vec pos/pos.vec-bg neg/neg.txt-numpos 100-numneg 2000-numstages 5-featuretyp E lbp-w 24-h 24-minhitrate 0.999-maxfalsealarmrate 0.1-mode All

• -numstages: Training Series A
• -numpos: Number of positive samples per level of training B
• a*b<= Total Positive Sample number
• -numneg: Number of negative samples used per level of training C
• c<= Total Negative Sample number
• -featuretype: Detection Features
• Experience: LBP for rapid training and detection, hog can better adapt to different lighting, haar suitable for the environment more complex situation

Precautions

1. If an error occurs, first check whether the parameter set is correct, second, consider changing the machine, and finally consider the change of machine environment configuration.
2. If you stay at a certain training stage for a long time, it indicates that it is very possible to get into a cycle, and also indicates that the classifier has reached a better state at this stage, you can exit (CTR+C) and then reduce the training progression or adjust other parameters.
3. If the training effect is not ideal, consider increasing the sample set and modifying the relevant training parameters.

cascaded Classifier Training-----OpenCV