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cbpt
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Color Based Probabilistic Tracking
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cbpt
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# cbpt Color Based Probabilistic Tracking using Python + OpenCV Functional object tracking implementation of Perez et al.'s article entitled "Color Based Probabilistic Tracking", which uses a particle filter and histogram comparison for a robust object tracking. This program tries to mimic the algorithm descripted in the aforementioned article. Some features were however approximated. Some considerations: * Using the exact measure of similarity between the current and candidate histograms * For the control update: State is represented by the vector (x, y, square_size). The transition goes as follows: X[t+1] = X[t] + V[t]dt + N[t], where V[t] represents the current velocity of the state and N[t] is a gaussian vector. * For the histogram's computation, only considerable values of hue/saturation are taken into account (>20%). The histogram is normalized. * The ROI is computed by averaging the current distribution. * The first distribution is considered to be a distribution with all particles in the location of the first ROI's central points, to be given as input of the program.  ## Requirements Opencv: `pip install opencv-python`
