Visual-Tracking-Using-MeanShift

Mean-Shift (MS) Mean-Shift (MS) is widely known as one of the most basic yet powerful tracking algorithms. Mean- Shift considers feature space as an empirical probability density function (pdf). If the input is a set of points then MS considers them as sampled from the underlying pdf. If dense regions (or clusters) are present in the feature space, then they correspond to the local maxima of the pdf. For each data point, MS associates it with the nearby peak of the pdf As an example, you can see the car sequence in file “Mean_Shift_Tracking.m”. We want to track the car in this sequence. We first needed to define the initial patch of the car in the first frame of the sequence. And then the moving car patch will be estimated by using the Bhattacharya coefficient and the weights corresponding to the neighboring patches. It will be deeply explained in the report.