Tim Pham's profile - activity

I also use the second approach on creating positive images, artificially deformed samples are bad for training.

Anyway, I would like to know if those images size similar with each others?

let say, k_1.jpg, k_2.jpg, ..., k_6.jpg, do they have the same size 30x90? in case those images have different size, when you create samples, the object part will be cut off, so the detection is incorrect.

congratulation ! finally, you got your thing, in spite of bad detecting result, you obtained the cascade file. for the detecting result, I guess you were wrong in creating positive images, I may help you for the next step, so, how did you create positive images?

@marcello, why did you changed -maxFalseAlarmRate value?, it may be the cause of error, set the value to 0.5 and see how it goes..

Thank @StevenPuttemans for your kind answer, indeed, you're really a senior .

I have another question. Do you think that 20 training stages are effective enough for an object detection? suppose, we have enough positive images and negative images (2000 and 5000), if we train for more than 20 stages, for example 36 stages , traincascade result will make more sense. Am I right?

Thank @steven for your kind answer, indeed, you're really a senior .

I have another question. Do you think that 20 training stages are effective enough for an object detection? suppose, we have enough positive images and negative images (2000 and 5000), if we train for more than 20 stages, for example 36 stages , traincascade result will make more sense. Am I right?

Thank Batista, I understand, I know that you have read face detection a lot and you absolutely get my idea when I mention about the positive image size 92x112. it's famous size (lol).

recently, I have written a program that read the live camera, then I got the video file, later, I extract all necessary positive image and negative background images that I want to test in order to train cascade. but my program that use traincascade file is not detect well, its incorrect, I am in doubt about the way of creating pos & neg images for cascade traning. I meant that, all step for creating traincascade have been done, I am doing object detection in live camera but the result for object detection is incorrect.

@Pedro Batista , give me some advice

I have read some post in this forum, the negative image sizes are free to collect. you can take them randomly.

Thank Batista,

You are right, I have used too much negative mages. I have changed the detection window size to 20x20, then speed have improved, it took 10 minutes for 20 stages for 360 postiive images and 900 negative images

The parameters:

opencv_traincascade -data caswork

-vec veccaswork.vec

-bg negcaswork.dat

-numPos 360

-numNeg 900

-numStages 20

-precalcValBufSize 2048

-precalcIdxBufSize 2048

-baseFormatSave

-minHitRate 0.999

-maxFalseAlarmRate 0.5

-w 20 -h 20

-mode ALL

Is it possible to detect object size 92x112 if I use 20x20 size for window detection?

Hi @steven, do you think the changing image width and height when create vector affect to detect result? as your mention earlier, size 80x80 is quite big. but my object window size is 80x80, does 20x20 size meet my requirement? Regarding to this LBP option, did you use -baseFormatSave option along with LBP?

and YES, @steven, you are right, LBP works just fine without -baseFormatSave.

@marcello, I appreciated about your "we", may I join to that ? lol

OK, Marcello, I have just pointed out some of my work experience that helped me get rid of the error, but I think my situation differs from you. it maybe your posNum and negNum are not good enough. yesterday, I have run full of 20 stages successfully. with 360 pos images and 900 neg images.

Believe me ,LBP option will not work on version 2.4.13, I have debugged that option.

Thank Steven

Hi Steven, are you sure LBP works fine on version 2.4.13? have you taken a look at the code of trancascade?

As my experience, I would like you to change some points below

• numNeg should be greater than numPos, for example, numPos = 600, numNeg=1500 => if you have 2000 pos images, you need 5000 neg images, if you have 1400 neg images, you need 560 pos images
• the images path should be in full path, you should apply find command with full path, use full path for vector file as well. after creating negative and positive files, delete the last blank line in those files.
• dont use LBP, as to my test, I have tested for 20 stages, but the 19th stage had been failed, version 2.4.x not use LBP, I recommend you not to use this option, otherwise your work will not be valuable

hope this help. Tim