Hi every body...
i have trained a model for license plate detection using a set of frontal images
where the plates are in acceptable conditions.
But now i want to test the model on plates with various types of skewness and rotation and sizes. taken with cameras form with different views and different zoom levels.
My idea was to transform my own current data to produce images similar to my test data. but it does not work for images with different zooms.
The problem is that the detected bounding boxes are small for many of the plates.
I am using ACFDetector toolbox version 3.50 on Matlab R2016a Windows 8.1;
objects are plates (cars in road , ground truth is of the size of plates and with padding car and road is in it too)
feature selection : Luv color, Gradient magnitude, Gradiant Histogram. (in one model I use Hog too)
In ACFDetector the size of trained sliding-window object detector is in fixed size.
Can I change the size of it in one image with fixed size before rescale it in pyramyd(Sth like what happen in Viola and Jones Detector and in ICFDetector).
which parameter do you offer to set and change for this issue.
3.What part of model training is more important(Classification, Feature Extraction, Size of GT, Train Date, Number of Train Data and ect.) for my problem.
Hi every body... i have trained a model for license plate detection using a set of frontal images where the plates are in acceptable conditions. But now i want to test the model on plates with various types of skewness and rotation and sizes. taken with cameras form with different views and different zoom levels.
My idea was to transform my own current data to produce images similar to my test data. but it does not work for images with different zooms. The problem is that the detected bounding boxes are small for many of the plates.
I am using ACFDetector toolbox version 3.50 on Matlab R2016a Windows 8.1; objects are plates (cars in road , ground truth is of the size of plates and with padding car and road is in it too) feature selection : Luv color, Gradient magnitude, Gradiant Histogram. (in one model I use Hog too)
thank you so much....