Tracking objects in complex dynamic environments can be less challenging once their behavior is recognized. Inferring on targets’ future actions based on their past can be addressed via probabilistic reasoning. Context information plays a crucial role in the reasoning process as it provides additional clues about targets’ behavior. Combining context reasoning with target tracking continues to increase with the availability of supporting
information. The framework here discussed views target’s actions as a Hidden Markov Model (HMM) with relevant context associated with each node. Context is at each time step selected based on immediate and goal driven sets of actions. Inference in the HMM is conditioned on prior target’s measurements and the belief state conditioned on context. This posterior is then compared with the target’s state estimate in order to adjust the
switching probability in the Interactive Multiple Models (IMM) tracking process.
