I have a sequence of images, and I need to find and track the creation of the objects, then their movement and then their disappearance. There can be up to $3$ objects overall, and sometimes there are fewer, or none. Between consecutive images there is a maximum distance that an object can move.
The practice I use is as follows.
Using a neural network, I estimate the locations for the objects in each image separately, up to $3$ locations in each image
Filtering out clear mistakes, e.g., random locations with no continuation over time.
After a little research, I found that with some effort I can translate this problem into a hidden Markov model, and this one can be solved with the Viterbi algorithm. The problem is that for each image there are more than $100$ possible object locations, and with $3$ objects we get over $10^5$ different states.
Is there a designated algorithm for such object-tracking over time? Or otherwise, if there's a good and efficient way I can fit Viterbi algorithm for this problem?
