An Object-Tracking Algorithm Based on Multiple-Model Particle Filtering With State Partitioning

As evidenced by the recent works of many researchers, the particle-filtering (PF) framework has revolutionized probabilistic visual target tracking. In this paper, we present a new particle filter tracking algorithm that incorporates the multiple-model (MM) paradigm and the technique of state partit...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 58; H. 5; S. 1797 - 1809
Hauptverfasser: Yan Zhai, Yeary, M.B., Cheng, S., Kehtarnavaz, N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2009
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Cameras
Dynamic models
Filtering
Filtering algorithms
Filtration
Human subjects
Humans
Instrumentation
Multiple dynamic models
particle filtering (PF)
Particle filters
Particle tracking
Partitioning
Partitioning algorithms
Proposals
State estimation
Studies
surveillance systems
Target tracking
Testing
Tracking
visual target tracking
ISSN:0018-9456, 1557-9662
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Zusammenfassung:As evidenced by the recent works of many researchers, the particle-filtering (PF) framework has revolutionized probabilistic visual target tracking. In this paper, we present a new particle filter tracking algorithm that incorporates the multiple-model (MM) paradigm and the technique of state partitioning with parallel filters. Traditionally, most tracking algorithms assume that a target operates according to a single dynamic model. However, the single-model assumption can cause the tracker to become unstable, particularly when the target has complex motions and when the camera has abrupt ego-motions. In the new tracking algorithm, a target was assumed to operate according to one dynamic model from a finite set of models. The switching process from one model to another was governed by a jump Markov process . Based on the improved MM particle filter framework, we offer a new design strategy that adopts the state-partitioning technique and a bank of parallel extended Kalman filters to construct a better proposal distribution to achieve further estimation accuracy. We have conducted extensive testing for the proposed tracking algorithm, and key outcomes were given in the results section. It has been demonstrated by the experiments that this approach gave significantly improved estimations, enabling the new particle filter to effectively track human subjects.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2009.2014511