Efficient bridging-based destination inference in object tracking
- Resource Type
- Conference
- Authors
- Ardeshiri, Tohid; Ahmad, Bashar I.; Langdon, Patrick M.; Godsill, Simon J.
- Source
- 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on. :4406-4410 Mar, 2017
- Subject
- Signal Processing and Analysis
Computational modeling
Kalman filters
Target tracking
Predictive models
Smoothing methods
Trajectory
Human computer interaction
Intent inference
Kalman filter
bridging distributions
human computer interactions
- Language
- ISSN
- 2379-190X
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.