On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation

We note that likelihood inference can be based on an unbiased simulation-based estimator of the likelihood when it is used inside a Metropolis–Hastings algorithm. This result has recently been introduced in statistics literature by Andrieu, Doucet, and Holenstein (2010, Journal of the Royal Statistical Society, Series B, 72, 269–342) and is perhaps surprising given the results on maximum simulated likelihood estimation. Bayesian inference based on simulated likelihood can be widely applied in microeconomics, macroeconomics, and financial econometrics. One way of generating unbiased estimates of the likelihood is through a particle filter. We illustrate these methods on four problems, producing rather generic methods. Taken together, these methods imply that if we can simulate from an economic model, we can carry out likelihood–based inference using its simulations.

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Deformable object model matching by topological and geometric similarity

Storytelling in Medicine ◽

10.1109/cgi.2004.1309230 ◽

2004 ◽

Author(s):

Kwok-Leung Tam ◽

R.W.H. Lau ◽

Chong-Wah Ngo

Keyword(s):

Object Model ◽

Model Matching ◽

Deformable Object ◽

Geometric Similarity

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Deformable Object Tracking Using Clustering and Particle Filter

Computing and Informatics ◽

10.4149/cai_2018_3_717 ◽

2018 ◽

Vol 37 (3) ◽

pp. 717-736 ◽

Cited By ~ 1

Author(s):

Muhammad Aasim Rafique ◽

Moongu Jeon ◽

Malik Tahir Hassan

Keyword(s):

Particle Filter ◽

Object Tracking ◽

Deformable Object

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Acquisition of Elastically Deformable Object Model Based on Measurement

Haptics: Perception, Devices, Mobility, and Communication - Lecture Notes in Computer Science ◽

10.1007/978-3-642-31401-8_19 ◽

2012 ◽

pp. 205-217

Author(s):

Koichi Hirota ◽

Kazuyoshi Tagawa

Keyword(s):

Object Model ◽

Deformable Object ◽

Model Based

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Dynamic Object Tracking Based on KAZE Features and Particle Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.2702 ◽

2014 ◽

Vol 556-562 ◽

pp. 2702-2706

Author(s):

Ying Xia ◽

Xin Hao Xu

Keyword(s):

Particle Filter ◽

Object Tracking ◽

Scale Invariance ◽

Experimental Results ◽

Object Model ◽

Dynamic Object ◽

Color Features ◽

Dynamic Tracking ◽

Object Appearance ◽

Accuracy And Stability

Accuracy and stability is crucial for dynamic object tracking. Considering the scale invariance, rotational invariance and strong anti-jamming capability of KAZE features, a method of dynamic object tracking based on KAZE features and particle filter is proposed. This method obtains the global color features of the dynamic object appearance and extracts its local KAZE features to construct the object model first, and then performs dynamic tracking by particle filter. Experimental results demonstrate the accuracy and stability of the proposed method.

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Methodology on Establishing Multivariate Failure Thresholds for Improved Remaining Useful Life Prediction in PHM

Annual Conference of the PHM Society ◽

10.36001/phmconf.2021.v13i1.3007 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Wenzhe Li ◽

Xiaodong Jia ◽

Yuan-Ming Hsu ◽

Youwen Liu ◽

Jay Lee

Keyword(s):

Particle Filter ◽

Health Management ◽

Likelihood Estimation ◽

Remaining Useful Life ◽

Function Parameter ◽

Copula Model ◽

Single Variable ◽

Future System ◽

Multiple Variables ◽

Useful Life

Prognostics and Health Management (PHM) methodologies and techniques have been much widely studied in the academia and practiced by the industry in recent years. Prognostic approaches commonly try to establish the relationship between Remaining Useful Life (RUL) and a single variable or health indicator (HI) which can be obtained from multi-sensor fusion or data-driven models. However, simply relying on a single variable could reduce RUL prediction robustness when it is less representative of the system health conditions. Taking multiple variables into consideration for RUL prediction, quantifying operating risks and determining multivariate failure threshold is essential yet rarely studied. Generally, there are three major challenges that limit the practicality of this topic. 1) How to determine the multivariate failure threshold? 2) How to quantify operation risks based on multiple variables? 3) How to make reliable extrapolations of future conditions? To address these questions, this paper proposes 1) a novel copula model to determine multivariate failure threshold, and 2) a Maximum Likelihood Estimation enhanced similarity-based Particle Filter (MLE-SMPF) to predict future system conditions. In the proposed methodology, the health assessment is firstly performed to obtain HI trajectory. The copula risk quantification model is then trained by two variables HI and life. The proposed copula model can easily include multiple variables compared with our previously published approach using bivariate Weibull Distribution[1]. Afterward, MLE-SMPF is used to extrapolate future HI for testing data. The prediction capability is further improved compared with [2] by introducing MLE for Particle Filter transition function parameter initialization. Finally, the system RUL is determined from the failure threshold which is obtained according to the quantified operation risk. The proposed methodology is validated on the C-MAPSS data from the PHM data competition 2008 hosted by PHM society. The result outperforms most of the benchmarks from recent publications. The proposed methodology is easy to transfer to other potential machine prognostic applications.

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