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I need to implement a kalman filter in python, however the code for the Hungarian Algorithm is different from the C++ example that I found here, can anyone tell me what result will be return when this code is call?

AssignmentProblemSolver APS;
APS.Solve(Cost,assignment,AssignmentProblemSolver::optimal);

I think the Cost will return a minimum Cost matrix, but what about the assignment variable?

Thank you, I'll check out the example

However is it ok if I use the python version as an example? which is the lk_track.py (I do notice it lack of some function compare to c++ version), since I don't have much knowledge on c++

does the 3rd point means that, after getting the new location of the points for the object, I need to get the average of all these points by comparing it with the previous points in the object?

There are quite a lot of point that it detected, both the object and the whole image.

Does this mean that, I should try to applying the LK optical flow on those detected object first, and then try to find the center point?

And about the texture, will the video quality affect the texture? I'm currently testing with a pedestrian video I found on YouTube so the quality of the video is not that great.

Currently, I had use the cv2.moment to get the center point of the targets, however when I apply calcOpticalFLowPyrLK to track these point, the tracking result is not very good, sometime it doesn't even manage to track the point.

I had also try using cv2.goodFeaturesToTrack to get the point, with this function, the calcOpticalFLowPyrLK is able to track the point, however there are too many unwanted point.

Can anyone suggested me a way to overcome this problem?