Efficient bridging-based destination inference in object tracking

Tohid Ardeshiri; Bashar I. Ahmad; Patrick M. Langdon; Simon J. Godsill

ICASSP 2017accepted0 citations

Efficient bridging-based destination inference in object tracking

Tohid Ardeshiri, Bashar I. Ahmad, Patrick M. Langdon, Simon J. Godsill

Abstract

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.

BibTeX

@inproceedings{icassp2017_efficientbridgin,
  title = {Efficient bridging-based destination inference in object tracking},
  author = {Tohid Ardeshiri and Bashar I. Ahmad and Patrick M. Langdon and Simon J. Godsill},
  booktitle = {ICASSP 2017},
  year = {2017}
}