Kalman filter for tracking obstacles 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
You might consider a particle filter.  Here's a link to a paper I wrote about tracking objects in video using a particle filter.  The great thing about these is that objects can be tracked through temporary occlusions.  The trick with using a Kalman filter here is dealing with the nonlinearity introduced by the edges of the video field, and casting your measurement model as a linear combination of your states (presumably x,y location).  The Unscented Kalman filter is another route you could go, it doesn't require linear models.