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Hello, I'm trying to train a classifier which detects all these kinds of traffic signs.

I've trained a classifier that detects all these signs, but it also detects other signs or background. I got many false detects. Im using 300 cropped images of signs (around 15 for each type of sign) and 5900 neg images of roads, cities(without signs ofc). What Im doing wrong? My parameters:

opencv_createsamples.exe -info info.txt -vec vector.vec -w 30 -h 30

opencv_traincascade.exe -data cascade/ -vec vector.vec -bg bg.txt -numPos 300 -numNeg 5900 -numStages 9 -featureType LBP -mode ALL -w 30 -h 30 -precalcValBufSize 2048 -precalcIdxBufSize 2048 -numThreads 4

Hello, I'm trying to train a classifier which detects all these kinds of traffic signs.

I've trained a classifier that detects all these signs, but it also detects other signs or background. I got many false detects. Im using 300 cropped images of signs (around 15 for each type of sign) and 5900 neg images of roads, cities(without signs ofc). What Im doing wrong? My parameters:

opencv_createsamples.exe -info info.txt -vec vector.vec -w 30 -h 30

opencv_traincascade.exe -data cascade/ -vec vector.vec -bg bg.txt -numPos 300 -numNeg 5900 -numStages 9 -featureType LBP -mode ALL -w 30 -h 30 -precalcValBufSize 2048 -precalcIdxBufSize 2048 -numThreads 4

Hello, I'm trying to train a classifier which detects all these kinds of traffic signs.

I've trained a classifier that detects all these signs, but it also detects other types of signs or background. I got many false detects. Im using 300 cropped images of signs (around 15 for each type of sign) and 5900 neg images of roads, cities(without signs ofc). What Im doing wrong? My parameters:

opencv_createsamples.exe -info info.txt -vec vector.vec -w 30 -h 30

opencv_traincascade.exe -data cascade/ -vec vector.vec -bg bg.txt -numPos 300 -numNeg 5900 -numStages 9 -featureType LBP -mode ALL -w 30 -h 30 -precalcValBufSize 2048 -precalcIdxBufSize 2048 -numThreads 4

Hello, I'm trying to train a classifier which detects all these kinds of traffic signs.

I've trained a classifier (9 stages) that detects all these signs, but it also detects other types of signs or background. I got many false detects. Im using 300 cropped images of signs (around 15 for each type of sign) and 5900 neg images of roads, cities(without signs ofc). What Im doing wrong? My parameters:

opencv_createsamples.exe -info info.txt -vec vector.vec -w 30 -h 30

opencv_traincascade.exe -data cascade/ -vec vector.vec -bg bg.txt -numPos 300 -numNeg 5900 -numStages 9 20 -featureType LBP -mode ALL -w 30 -h 30 -precalcValBufSize 2048 -precalcIdxBufSize 2048 -numThreads 4

Hello, I'm trying to train a classifier which detects all these kinds of traffic signs.

I've trained a classifier (9 stages) that detects all these signs, but it also detects other types of signs or background. I got many false detects. Im using 300 cropped images of signs (around 15 for each type of sign) and 5900 neg images of roads, cities(without signs ofc). What Im doing wrong? wrong? My parameters:

opencv_createsamples.exe -info info.txt -vec vector.vec -w 30 -h 30

opencv_traincascade.exe -data cascade/ -vec vector.vec -bg bg.txt -numPos 300 -numNeg 5900 -numStages 20 -featureType LBP -mode ALL -w 30 -h 30 -precalcValBufSize 2048 -precalcIdxBufSize 2048 -numThreads 4

d1.detectMultiScale(image2, znaki, 1.04, 5, CV_HAAR_SCALE_IMAGE, Size(30, 30));

Hello, I'm trying to train a classifier which detects all these kinds of traffic signs.

I've trained a classifier (9 stages) that detects all these signs, but it also detects other types of signs or background. I got many false detects. Im using 300 cropped images of signs (around 15 for each type of sign) and 5900 neg images of roads, cities(without signs ofc). What Im doing wrong? wrong? Maybe it's not good idea to use cascade classifier for my purpose? My parameters:

opencv_createsamples.exe -info info.txt -vec vector.vec -w 30 -h 30

opencv_traincascade.exe -data cascade/ -vec vector.vec -bg bg.txt -numPos 300 -numNeg 5900 -numStages 20 -featureType LBP -mode ALL -w 30 -h 30 -precalcValBufSize 2048 -precalcIdxBufSize 2048 -numThreads 4

d1.detectMultiScale(image2, znaki, 1.04, 5, CV_HAAR_SCALE_IMAGE, Size(30, 30));