Tracking Pedestrians from a Moving Car

Can someone help me, What is the Matlab code syntax for calling the Kalman filter?

Using the sample code in this link: Tracking Pedestrians from a Moving Car - MATLAB & Simulink (mathworks.com)

Answer 1

[Kevin Holly]

Here the Kalman filter object is created

% Create a Kalman filter object.
kalmanFilter = configureKalmanFilter('ConstantVelocity', ...
    centroid, [2, 1], [5, 5], 100);

https://www.mathworks.com/help/vision/ref/configurekalmanfilter.html

% Create an array of tracks, where each
% track is a structure representing a moving object in the video.
function tracks = initializeTracks()
        % Create an empty array of tracks
        tracks = struct(...
            'id', {}, ...
            'color', {}, ...
            'bboxes', {}, ...
            'scores', {}, ...
            'kalmanFilter', {}, ...
            'age', {}, ...
            'totalVisibleCount', {}, ...
            'confidence', {}, ...            
            'predPosition', {});
    end

You can open the KalmanFilter object by:

open vision.KalmanFilter

Within it, you can see the different functions that can be called.

%   predict method syntax:
% 
%   [z_pred, x_pred, P_pred] = predict(obj) returns the prediction of
%   measurement, state, and state estimation error covariance at the next
%   time step (e.g., next video frame). The internal state and covariance
%   of Kalman filter are overwritten by the prediction results.
% 
%   [z_pred, x_pred, P_pred] = predict(obj, u) additionally, lets you
%   specify the control input, u, an L-element vector. This syntax applies
%   if you have set the control model B.
%
%   correct method syntax:
% 
%   [z_corr, x_corr, P_corr] = correct(obj, z) returns the correction of
%   measurement, state, and state estimation error covariance based on the
%   current measurement z, an N-element vector. The internal state and
%   covariance of Kalman filter are overwritten by the corrected values.
% 
%   distance method syntax:
% 
%   d = distance(obj, z_matrix) computes a distance between one or more
%   measurements supplied by the z_matrix and the measurement predicted by
%   the Kalman filter object. This computation takes into account the
%   covariance of the predicted state and the process noise. Each row of
%   the input z_matrix must contain a measurement vector of length N. The
%   distance method returns a row vector where each element is a distance
%   associated with the corresponding measurement input. The distance
%   method can only be called after the predict method.
%
%   Notes:
%   ------
%   - If the measurement exists, e.g., the object has been detected, you
%     can call the predict method and the correct method together. If the
%     measurement is missing, you can call the predict method but not the
%     correct method. 
%
%       If the object is detected
%           predict(kalmanFilter);
%           trackedLocation = correct(kalmanFilter, objectLocation);
%        Else
%           trackedLocation = predict(kalmanFilter);
%        End
%
%   - You can use the distance method to compute distances that describe
%     how a set of measurements matches the Kalman filter. You can thus
%     find a measurement that best fits the filter. This strategy can be
%     used for matching object detections against object tracks in a
%     multi-object tracking problem.
%
%   - You can use configureKalmanFilter to create a Kalman filter for
%     object tracking.
%
%   KalmanFilter methods:
% 
%   predict  - Predicts the measurement, state, and state estimation error covariance
%   correct  - Corrects the measurement, state, and state estimation error covariance
%   distance - Computes distances between measurements and the Kalman filter
%   clone    - Creates a tracker object with the same property values
% 

So, in the case above,

predict(kalmanFilter)
correct(kalmanFilter)

or if you are pulling the kalmanFilter from the newTrack:

predict(track(i).kalmanFilter)
correct(track(i).kalmanFilter)

where i is the index of the array of tracks

If you are unfamilar with object-oriented programming, I would take the free onramp training:

https://matlabacademy.mathworks.com/details/object-oriented-programming-onramp/oroop