Efficient bridging-based destination inference in object tracking
Tohid Ardeshiri, Bashar I. Ahmad, Patrick Langdon, Simon Godsill · 2017
This paper proposes a probabilistic intent inference approach that is significantly more computationally efficient than other existing bridging-distributions-based predictors. It sequentially determines the probabilities of all possible destinations of a tracked object, whose motion is modelled by a Markov chain with the distribution of its terminal state equal to that of a nominal endpoint. This encapsulates the long term dependencies in the object trajectory as dictated by intent. Simulations using real data show that the notable reductions in computations achieved by the introduced bridging-based predictor does not impact the quality of the overall inference results.