The hungarian algorithm, also known as Kuhn-Munkres algorithm, can associate an obstacle from one frame to another, based on a score. We have many scores we can think of :
IOU (Intersection Over Union); meaning that if the bounding box is overlapping the previous one, it’s probably the same.
Shape Score ; if the shape or size didn’t vary too much during two consecutives frames; the score increases.
Convolution Cost ; we could run a CNN (Convolutional Neural Network) on the bounding box and compare this result with the one from a frame ago. If the convolutional features are the same, then it means the objects looks the same. If there is a partial occlusion, the convolutional features will stay partly the same and association will remain.
source:https://towardsdatascience.com/computer-vision-for-tracking-8220759eee85?fbclid=IwAR2ksoHRNrwL6r-MzKAvvPycCmPuJqDPb_2MpYZutzjcnxLNkOFDQrf5Smo
