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I'm developing a Computer Vision project in Matlab as an aid to visually impaired people. The setup would be a stereo pair of cameras which the blind person would carry. Using this stereo information, I generate a disparity image using Semi-Global Block Matching (SGBM). After that, I process a "virtual disparity", which basically is a homogeneous transformation, to pass the image plane to the ground, resulting in an image like the following:

Now, After removing the ground plane, morphologically improve the image, and threshold it to keep only near obstacles, I get a foreground mask with the approximate shape of the objects.

I was thinking on tracking each one of the obstacles to improve the detection. Considering that the camera is not fixed, since the blind person carrying it is moving, I wonder if the relative movement of all the obstacles in the image would be easily followed by a Kalman filter, or it would be better a feature based tracker like Kanade–Lucas–Tomasi feature tracker (KLT).

Thank you in advance

I'm developing a Computer Vision project in Matlab as an aid to visually impaired people. The setup would be a stereo pair of cameras which the blind person would carry. Using this stereo information, I generate a disparity image using Semi-Global Block Matching (SGBM). After that, I process a "virtual disparity", which basically is a homogeneous transformation, to pass the image plane to the ground, resulting in an image like the following:

Now, After removing the ground plane, morphologically improve the image, and threshold it to keep only near obstacles, I get a foreground mask with the approximate shape of the objects.

I was thinking on tracking each one of the obstacles to improve the detection. Considering that the camera is not fixed since the blind person carrying it is moving, I wonder if the relative movement of all the obstacles in the image would be easily followed by a Kalman filter, or it would be better a feature-based tracker like Kanade–Lucas–Tomasi feature tracker (KLT).

Thank you in advance

I'm developing a Computer Vision project in Matlab as an aid to visually impaired people. The setup would be a stereo pair of cameras which the blind person would carry. Using this stereo information, I generate a disparity image using Semi-Global Block Matching (SGBM). After that, I process a "virtual disparity", which basically is a homogeneous transformation, to pass the image plane to the ground, resulting in an image like the following:

Now, After removing the ground plane, morphologically improve the image, and threshold it to keep only near obstacles, I get a foreground mask with the approximate shape of the objects.

I was thinking on tracking each one of the obstacles to improve the detection. Considering that the camera is not fixed, since the blind person carrying it is moving, I wonder if the relative movement of all the obstacles in the image would be easily followed by a Kalman filter, or it would be better a feature based tracker like Karhunen-Loeve Transform (KLT).

Thank you in advance

I'm developing a Computer Vision project in Matlab as an aid to visually impaired people. The setup would be a stereo pair of cameras which the blind person would carry. Using this stereo information, I generate a disparity image using Semi-Global Block Matching (SGBM). After that, I process a "virtual disparity", which basically is a homogeneous transformation, to pass the image plane to the ground, resulting in an image like the following:

Now, After removing the ground plane, morphologically improve the image, and threshold it to keep only near obstacles, I get a foreground mask with the approximate shape of the objects.

I was thinking on tracking each one of the obstacles to improve the detection. Considering that the camera is not fixed, since the blind person carrying it is moving, I wonder if the relative movement of all the obstacles in the image would be easily followed by a Kalman filter, or it would be better a feature based tracker like Kanade–Lucas–Tomasi feature tracker (KLT).

Thank you in advance

I'm developing a Computer Vision project in MatLab as an aid to visually impaired people. The setup would be a stereo pair of cameras which the blind person would carry. Using this stereo information, I generate a disparity image using SGBM. After that, I process a "virtual disparity", which basically is a homogeneous transformation, to pass the image plane to the ground, remaining a image like the following:

Now, After removing the ground plane, morphologically improve the image, and threshold it to keep only near obstacles, I get a foreground mask with the aproximate shape of the objects.

I was thinking on tracking each one of the obstacles to improve the detection. Considering that the camera is not fixed, since the blind person carrying it is moving, I wonder if the relative movement of all the obstacles in the image would be easily followed by a Kalman filter, or it would be better a feature based tracker like KLT.

Thank you in advance

I'm developing a Computer Vision project in Matlab as an aid to visually impaired people. The setup would be a stereo pair of cameras which the blind person would carry. Using this stereo information, I generate a disparity image using Semi-Global Block Matching (SGBM). After that, I process a "virtual disparity", which basically is a homogeneous transformation, to pass the image plane to the ground, resulting in an image like the following:

Now, After removing the ground plane, morphologically improve the image, and threshold it to keep only near obstacles, I get a foreground mask with the approximate shape of the objects.

I was thinking on tracking each one of the obstacles to improve the detection. Considering that the camera is not fixed, since the blind person carrying it is moving, I wonder if the relative movement of all the obstacles in the image would be easily followed by a Kalman filter, or it would be better a feature based tracker like Karhunen-Loeve Transform (KLT).

Thank you in advance