Fat and thin adaptive HMM filters for vision based detection of moving targets
Wainwright, Alexander Lloyd, Ford, Jason J., & Lai, John S. (2011) Fat and thin adaptive HMM filters for vision based detection of moving targets. In Drummond, Tom (Ed.) Proceedings of the ACRA 2011 Conference, Australian Robotics & Automation Association, Monash University, Melbourne, VIC, pp. 1-10.
Computer vision is an attractive solution for uninhabited aerial vehicle (UAV) collision avoidance, due to the low weight, size and power requirements of hardware. A two-stage paradigm has emerged in the literature for detection and tracking of dim targets in images, comprising of spatial preprocessing, followed by temporal ﬁltering. In this paper, we investigate a hidden Markov model (HMM) based temporal ﬁltering approach. Speciﬁcally, we propose an adaptive HMM ﬁlter, in which the variance of model parameters is reﬁned as the quality of the target estimate improves. Filters with high variance (fat ﬁlters) are used for target acquisition, and ﬁlters with low variance (thin ﬁlters) are used for target tracking. The adaptive ﬁlter is tested in simulation and with real data (video of a collision-course aircraft). Our test results demonstrate that our adaptive ﬁltering approach has improved tracking performance, and provides an estimate of target heading not present in previous HMM ﬁltering approaches.
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|Item Type:||Conference Paper|
|Keywords:||Autonomous Vehicles, Machine Vision, Hidden Markov Model|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100) > Avionics (090105)|
|Divisions:||Current > Research Centres > Australian Research Centre for Aerospace Automation
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2011 [please consult the author]|
|Deposited On:||10 Nov 2011 23:57|
|Last Modified:||19 Sep 2014 21:43|
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