A singlemultiple tracking algorithm that can deal with simple situations eg occlusion deformation and so on

One example of a tracking algorithm that can handle simple situations such as occlusion and deformation is the Kalman Filter. The Kalman Filter is a mathematical algorithm that uses a series of measurements to estimate the state of a system over time. It is particularly effective in situations where there is noise or uncertainty in the measurements, as it can use previous measurements to refine its estimate of the current state.

In the context of object tracking, the Kalman Filter can be used to predict the future location of an object based on its previous trajectory, and to update its estimate as new measurements become available. The Kalman Filter can also handle occlusion and deformation by using a more complex model of the object's motion, which takes into account the possibility of sudden changes in position or shape.

Overall, the Kalman Filter is a versatile and effective algorithm for tracking objects in a variety of simple situations, and it can be easily adapted to handle more complex scenarios as well