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2012-07-31 08:09:46 -0500 commented answer Human detector using HAAR cascades has too many false positives it is confident about

... when the cascades have finished training and i can see whether that improves it. In the meantime perhaps you could answer another question that came to my mind. Should there be made some kind of preprocessing? I read that for face detection it helps to equalize the histogramm after converting to grayscale. So is there something that could help here? Is there somesthing you could preprocess when using HOG? Or should i insert this as a new question?

2012-07-31 08:00:08 -0500 commented answer Human detector using HAAR cascades has too many false positives it is confident about

Hello, sorry for my late response and thank you for your answer. I knew that HAAR isn't ideal for human detection due to the variance in clothing but in my understanding it shouldn't hit where the region was clearly in the negative samples. The images i generate from the sample-videos are kind of unique. In the videos different people walk through the room. So the limbs have different configurations and the persons have different orientations in the room. But since i save every frame there are a lot of samples that are similar to each other because from one frame to the next the person just moved a little bit. I tried to use HOG as well and with it i have the same problem just less false positives. I now have tied to add noise to the background as i described above. I will report back...

2012-07-24 04:12:35 -0500 asked a question Human detector using HAAR cascades has too many false positives it is confident about


i am trying to train a HAAR-cascade to detect people. For this i generated positive samples from an recorded video by foreground-background-subtraction. I used pictures of the empty room with different arrangements and different lighting as negative samples. The trained cascade seems to detect a person with an acceptable rate. But the cascade also has a lot of false positives in regions that were not rearranged in the different negative samples. For example there is a table that could not be moved. The cascade is constantly marking the corner of the table although that table is in the negative samples and even is in EVERY negative sample. I tried different approaches to lower the false-alarm-rate but i was not very successfull. I tried to increase the number of stages to 35-40. At first that seemed to help but when i increased the number of samples using different people to get more variance the false positives reappeared. My guess is that the surrounding background in the positive samples is the cause for the false positives since the samples are from that room as well and for example parts of that table are in some samples. In another attempt i tried to decrease the size of the region i cut from the frame to get less background in my positive samples. But if i am right and the background in the samples is the reason then that lesser background might still be too much. Since from the foreground-background-subtraction i have the backgroundpixels i thought about replacing them with white noise in the positive samples. Could that help or would it be harmful? Does anybody have an alternativ solution how i can lower the false-positive-rate? I would be thankful for any assistance.


2012-07-24 04:00:13 -0500 commented answer Earth Mover's Distance EMD() results in memory access error

Hello again, it calculated the signatures. It seems it was finished shortly after i checked the last time. The used memory was about 3gb but that seems reasonable since a matrix half the size seems to take around 1,5gb before i applied your patch to increase the buffersize. And the calculation-time of that half-size matrix is 1 hour 18 minutes. So it would seem that the problem just was the buffersize. But i still think that in such a case opencv should throw an exception instead of exiting with an memory access error. So i think that i will report this as a bug as soon as i have the time. Thanks again for your help! Kind regards Gerrit

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2012-07-23 15:49:22 -0500 commented answer Earth Mover's Distance EMD() results in memory access error

Hello Ilya, thanks for your help, it seems to at least solve the memory access error. I today made the changes and the calculation started without the error. But it takes a lot mor time to calculate. Most of my signatures take about 10 minutes. The one i tested on after three hours still wasn't finished. I'll let it calculate over night and see whether it finishes. Then i will re-check how long the signatures which are bigger than the ones that crashed before take to compute. For now it seems as if there still would be a bigger issue. In any way shouldn't there an exception be thrown? I will see the results tomorrow and will report back. Kind regards Gerrit

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2012-07-20 11:36:21 -0500 asked a question Earth Mover's Distance EMD() results in memory access error


i am trying to compute the Earth Mover's Distance using opencv's function EMD(). For most of my signatures that works fine. But for others i get an memory access error which i do not understand. At first i thought maybe the signature matrices were to big because they have 39015 rows and 3 columns and the other one has 9216 row and 3 cloumns. But that can not be the problem since a matrix with 36481 rows works fine. Until now i have not been able to tell exactly under which conditions i get this memory access error. But i was able to find the line in opencv where the error occures. That happens to be line 385 in OpenCV-2.4.2/modules/imgproc/src/emd.cpp. The error occours in earlier version like opencv 2.1. I call the function with "cv::EMD(signatureGroundTruthMat, signatureDetectionMat, CV_DIST_L2)". I could provide the matrices that cause the error if that would help. My question would be whether that is a bug in opencv that i should report or whether i have done something wrong with the matrices. Out of 1000 matrices i used EMD() on just a hand full produced that error. I would be thankful for any assistance