Bayesian Intent Prediction for Fast Maneuvering Objects using Variable Rate Particle Filters
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Publication Date
2019-10-01Journal Title
IEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN
2161-0363
ISBN
9781728108247
Volume
2019-October
Type
Conference Object
This Version
AM
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Gan, R., Liang, J., Ahmad, B., & Godsill, S. (2019). Bayesian Intent Prediction for Fast Maneuvering Objects using Variable Rate Particle Filters. IEEE International Workshop on Machine Learning for Signal Processing, MLSP, 2019-October https://doi.org/10.1109/MLSP.2019.8918811
Abstract
The motion of a tracked object often has long term underlying dependencies due to premeditated actions dictated by intent, such as destination. Revealing this intent, as early as possible, can enable advanced intelligent system functionalities for conflict/opportunity detection and automated decision making, for instance in surveillance and human computer interaction. This paper presents a novel Bayesian intent inference framework that utilises sequential Monte Carlo (SMC) methods to determine the destination of a tracked object exhibiting unknown jump behaviour. The latter can arise from the object undertaking fast maneuvers (e.g. for obstacle avoidance) and/or due to external uncontrollable environmental perturbations. Suitable intent-driven stochastic models and inference routines are introduced. The effectiveness of the proposed approach is demonstrated using synthetic and real data.
Identifiers
External DOI: https://doi.org/10.1109/MLSP.2019.8918811
This record's URL: https://www.repository.cam.ac.uk/handle/1810/297033
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