Problem in Matlab example Motion-Based Multiple Object Tracking

Hello. I am running MATLAB example Motion-Based Multiple Object Tracking and analyzing Kalman filter parameters. I mentioned that in vision.kalmanFilter, Kalman filter parameters are not updated in each time step. I expected that state and state covariance to be updated in each time step after correcting the Kalman filter with measurements, but this does not happen in the code. I saved some samples of Kalman filter parameters of one track in different time steps and uploaded the images. you can see that states are the same in frames 50-51-52. Can anyone help me to see the updated Kalman filter parameters?

Answer 1

[Namnendra]

Hi Alex,

When working with a Kalman filter, especially in the context of motion-based multiple object tracking, the parameters such as the state and state covariance should indeed update at each time step as new measurements are incorporated. If you observe that these parameters are not updating, there could be several reasons for this behavior. Here's how you can troubleshoot and ensure the Kalman filter parameters are updated correctly:

1. Check Measurement Incorporation: Ensure that the measurements are correctly incorporated into the Kalman filter update step. The Kalman filter typically consists of a prediction step and a correction (update) step. Verify that the correction step is being executed with the new measurements.

2. Verify Measurement Availability: Confirm that measurements are available at each time step. If no new measurements are provided, the filter may not perform an update, leading to unchanged state estimates.

3. Inspect Code Logic: Review the code to ensure that the Kalman filter update function is called appropriately. Look for any conditional statements that might be skipping the update step.

4. Debugging: Add debugging statements to print out the state and covariance matrix before and after the update step. This will help you trace whether the update step is being executed and if the parameters are changing.

5. Check for NaN or Inf Values: Ensure that the measurements and state predictions do not result in NaN or Inf values, which can cause the filter to fail silently.

6. Kalman Filter Configuration: Double-check the configuration of the Kalman filter, including the process noise covariance and measurement noise covariance. Incorrect settings can lead to poor filter performance and minimal updates.

7. MATLAB Example Code: If you are using a MATLAB example, ensure that you have not inadvertently modified the part of the code responsible for updating the Kalman filter.

8. Visualize the Process: If possible, visualize the tracking process and the measurements to see if the filter is receiving reasonable inputs and producing expected outputs.