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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.

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Abstract

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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ID Code: 46951
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
Funding:
Copyright Owner: Copyright 2011 [please consult the author]
Deposited On: 11 Nov 2011 09:57
Last Modified: 20 Sep 2014 07:43

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