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OpenCv 3 Beta -ICF classifier performance is very bad.

Hi,

I am working with OpenCV_Contrib module and using xobjdetect module for pedestrian detection. I have trained ICF model using following params--------

 icfParams.alpha= 0.02;
icfParams.bg_per_image= 5;
icfParams.feature_count= 30000;
icfParams.features_type= "icf";
icfParams.is_grayscale= false ;
icfParams.model_n_cols= 64;  // W-->Training Model Size ..Size of +Ve and -Ve image
icfParams.model_n_rows= 128; // H-->Training Model Size ..Size of +Ve and -Ve image
icfParams.use_fast_log= false;
icfParams.weak_count= 2000;

with Inrai data set- #positive image =2416, # no of neg=1218 ( total icfParams.bg_per_image *1218);

Using-

    vector<cv::Rect> objects;
vector<float> values;
float scaleFactor=1.09; // 2^1/8;
float threshold=0;
cv::Size minSize= cv::Size(24,48); //(width,height)
cv::Size maxSize= cv::Size(64,128);
cout <<minSize.height << minSize.width <<endl;
int slidingStep=8;

icfDetect.detect(img,objects,scaleFactor,minSize, maxSize, threshold, slidingStep,values);

I am trying to detect Pedestrian in given Image. Input Image- image description Ouput Image- image description

Plz someone suggest what is the issue?

Also, why this using waldboost+ stump instead of 3 level decision tree as suggested by author -DOllar in his paper- http://vision.ucsd.edu/sites/default/files/dollarBMVC09ChnFtrs_0.pdf few doubts-

How to train model in better ways. How to get better detection rate/window How to set these params.

OpenCv 3 Beta -ICF classifier performance is very bad.

Hi,

I am working with OpenCV_Contrib module and using xobjdetect module for pedestrian detection. I have trained ICF model using following params--------

params:

 icfParams.alpha= 0.02;
icfParams.bg_per_image= 5;
icfParams.feature_count= 30000;
icfParams.features_type= "icf";
icfParams.is_grayscale= false ;
icfParams.model_n_cols= 64;  // W-->Training Model Size ..Size of +Ve and -Ve image
icfParams.model_n_rows= 128; // H-->Training Model Size ..Size of +Ve and -Ve image
icfParams.use_fast_log= false;
icfParams.weak_count= 2000;

with Inrai data set- set

#positive image =2416, # no of neg=1218 ( total icfParams.bg_per_image *1218);

Using-

*1218);

For this I have been using the following software

 vector<cv::Rect> objects;
vector<float> values;
float scaleFactor=1.09; // 2^1/8;
float threshold=0;
cv::Size minSize= cv::Size(24,48); //(width,height)
cv::Size maxSize= cv::Size(64,128);
cout <<minSize.height << minSize.width <<endl;
int slidingStep=8;

icfDetect.detect(img,objects,scaleFactor,minSize, maxSize, threshold, slidingStep,values);

slidingStep,values);

I am trying to detect Pedestrian in given Image. Input Image-

INPUT IMAGE

image description Ouput Image-

OUTPUT IMAGE

image description

Plz someone suggest what is the issue?

issue? Also, why this using waldboost+ stump instead of 3 level decision tree as suggested by author -DOllar Dollar in his paper- http://vision.ucsd.edu/sites/default/files/dollarBMVC09ChnFtrs_0.pdf few paper at http://vision.ucsd.edu/sites/default/files/dollarBMVC09ChnFtrs_0.pdf

Few doubts-

  • How to train model in better ways. ways.
  • How to get better detection rate/window rate/window
  • How to set these params.