rueynshard's profile - activity

Try the omnidir:: namespace instead: http://docs.opencv.org/trunk/db/dd2/n..., and add RECTIFY::PERSPECTIVE as one of the flags.

You can try some of the following:

a) Take more chessboard images at the edges of the camera, and at more extreme angles

b) Use the omnidirectional camera methods - http://docs.opencv.org/trunk/dd/d12/t..., and select RECTIFY_PERSPECTIVE for the rectification type.

c) Use the cv::fisheye camera methods

d) Perhaps the camera has already been slightly undistorted/warped. If that's the case, try and see if you can get a pure/unmanipulated image.

I'm currently using HOG (Histogram of Oriented Gradients) to generate feature vectors for each of my training images, and then using this data to train an SVM Classifier. Since the SVM classifies the incoming data into one of 2 classes, I understand I need to provide training images for both classes.

I hope to use this to detect pedestrians, and I have a set of a few hundred 128x64 images of different pedestrians. However, I'm not sure what images to use for the other class, i.e. the non-positive one. Should I just use images of different ambient scenes with no pedestrians in them?

Thank you! This was really helpful

From my understanding, undistortPoints() takes a set of points on a distorted image, and calculates where their coordinates would be on an undistorted version of the same image. projectPoints() maps a set of object coordinates to their corresponding image coordinates.

However, I am unsure if projectPoints() maps the object coordinates to a set of image points on the distorted image (ie. the original image) or one that has been undistorted (straight lines)?

Furthermore, the OpenCV documentation for undistortPoints states that 'the function performs a reverse transformation to projectPoints()'. Could you please explain how this is so?