Atmospheric clouds are commonly encountered phenomena affecting visual tracking from air-borne or space-borne
sensors. Generally clouds are difficult to detect and extract because they are complex in shape and interact with sunlight
in a complex fashion. In this paper, we propose a clustering game theoretic image segmentation based approach to
identify, extract, and patch clouds. In our framework, the first step is to decompose a given image containing clouds. The
problem of image segmentation is considered as a “clustering game”. Within this context, the notion of a cluster is
equivalent to a classical equilibrium concept from game theory, as the game equilibrium reflects both the internal and
external (e.g., two-player) cluster conditions. To obtain the evolutionary stable strategies, we explore three evolutionary
dynamics: fictitious play, replicator dynamics, and infection and immunization dynamics (InImDyn). Secondly, we use
the boundary and shape features to refine the cloud segments. This step can lower the false alarm rate. In the third step,
we remove the detected clouds and patch the empty spots by performing background recovery. We demonstrate our
cloud detection framework on a video clip provides supportive results.
Many algorithms may be applied to solve the target tracking problem, including the Kalman Filter and different types of
nonlinear filters, such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF).
This paper describes an intelligent algorithm that was developed to elegantly select the appropriate filtering technique
depending on the problem and the scenario, based upon a sliding window of the Normalized Innovation Squared (NIS).
This technique shows promise for the single target, single radar tracking problem domain. Future work is planned to
expand the use of this technique to multiple targets and multiple sensors.