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Generic Tracking and probabilistic Prediction Framework and Its Application in Autonomous Driving

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2020年第9期21卷 3634-3649页

作者： Li, Jiachen Zhan, Wei Hu, Yeping Tomizuka, Masayoshi Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

Accurately tracking and predicting behaviors of surrounding objects are key prerequisites for intelligent systems such as autonomous vehicles to achieve safe and high-quality decision making and motion planning. However, there still remain challenges for multi-target tracking due to object number fluctuation and occlusion. To overcome these challenges, we propose a constrained mixture sequential Monte Carlo (CMSMC) method in which a mixture representation is incorporated in the estimated posterior distribution to maintain multi-modality. Multiple targets can be tracked simultaneously within a unified framework without explicit data association between observations and tracking targets. The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of the CMSMC method. An example in this paper is a learning-based model for hierarchical time-series prediction, which consists of a behavior recognition module and a state evolution module. Both modules in the proposed model are generic and flexible so as to be applied to a class of time-series prediction problems where behaviors can be separated into different levels. Finally, the proposed framework is applied to a numerical case study as well as a task of on-road vehicle tracking, behavior recognition, and prediction in highway scenarios. Instead of only focusing on forecasting trajectory of a single entity, we jointly predict continuous motions for interactive entities simultaneously. The proposed approaches are evaluated from multiple aspects, which demonstrate great potential for intelligent vehicular systems and traffic surveillance systems.

关键词： Target tracking Predictive models Monte Carlo methods Hidden Markov models probabilistic logic Trajectory Trajectory prediction multi-target tracking sequential Monte Carlo probabilistic graphical models deep learning bahavior recognition

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A nonparametric belief propagation method for uncertainty quantification with applications to flow in random porous media

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JOURNAL OF COMPUTATIONAL PHYSICS 2013年 250卷 616-643页

作者： Chen, Peng Zabaras, Nicholas Cornell Univ Sibley Sch Mech & Aerosp Engn Mat Proc Design & Control Lab Ithaca NY 14853 USA Cornell Univ Ctr Appl Math Ithaca NY 14853 USA

A probabilistic graphical model approach to uncertainty quantification for flows in random porous media is introduced. Model reduction techniques are used locally in the graph to represent the random permeability. Then the conditional distribution of the multi-output responses on the low dimensional representation of the permeability field is factorized into a product of local potential functions. An expectation-maximization algorithm is used to learn the nonparametric representation of these potentials using the given input/output data. We develop a nonparametric belief propagation method for uncertainty quantification by employing the loopy belief propagation algorithm. The nonparametric nature of our model is able to capture non-Gaussian features of the response. The proposed framework can be used as a surrogate model to predict the responses for new input realizations as well as our confidence on these predictions. Numerical examples are presented to demonstrate the accuracy and efficiency of the proposed framework for solving uncertainty quantification problems in flows through porous media using stationary and non-stationary permeability fields. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Uncertainty quantification Predictive modeling probabilistic graphical models Porous media flow Nonparametric belief propagation Loopy belief propagation

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Latent Hierarchical Model for Activity Recognition

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IEEE TRANSACTIONS ON ROBOTICS 2015年第6期31卷 1472-1482页

作者： Hu, Ninghang Englebienne, Gwenn Lou, Zhongyu Krose, Ben Univ Amsterdam Inst Informat NL-1098 XH Amsterdam Netherlands

We present a novel hierarchical model for human activity recognition. In contrast with approaches that successively recognize actions and activities, our approach jointly models actions and activities in a unified framework, and their labels are simultaneously predicted. The model is embedded with a latent layer that is able to capture a richer class of contextual information in both state-state and observation-state pairs. Although loops are present in the model, the model has an overall linear-chain structure, where the exact inference is tractable. Therefore, the model is very efficient in both inference and learning. The parameters of the graphical model are learned with a structured support vector machine. A data-driven approach is used to initialize the latent variables;therefore, no manual labeling for the latent states is required. The experimental results from using two benchmark datasets show that our model outperforms the state-of-the-art approach, and our model is computationally more efficient.

关键词： Human activity recognition personal robots probabilistic graphical models RGB-D perception

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Sum-product graphical models

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MACHINE LEARNING 2020年第1期109卷 135-173页

作者： Desana, Mattia Schnoerr, Christoph Heidelberg Univ Heidelberg Collaboratory Image Proc HCI Heidelberg Germany Heidelberg Univ Image & Pattern Anal Grp IPA Heidelberg Germany

This paper introduces a probabilistic architecture called sum-product graphical model (SPGM). SPGMs represent a class of probability distributions that combines, for the first time, the semantics of probabilistic graphical models (GMs) with the evaluation efficiency of sum-product networks (SPNs): Like SPNs, SPGMs always enable tractable inference using a class of models that incorporate context specific independence. Like GMs, SPGMs provide a high-level model interpretation in terms of conditional independence assumptions and corresponding factorizations. Thus, this approach provides new connections between the fields of SPNs and GMs, and enables a high-level interpretation of the family of distributions encoded by SPNs. We provide two applications of SPGMs in density estimation with empirical results close to or surpassing state-of-the-art models. The theoretical and practical results demonstrate that jointly exploiting properties of SPNs and GMs is an interesting direction of future research.

关键词： Sum product networks probabilistic graphical models Density estimation Deep learning Exact inference Density estimation

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Exploiting semantic knowledge for robot object recognition

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KNOWLEDGE-BASED SYSTEMS 2015年第Sep.期86卷 131-142页

作者： Ruiz-Sarmiento, Jose-Raul Galindo, Cipriano Gonzalez-Jimenez, Javier Univ Malaga Syst Engn & Automat Dept E-29071 Malaga Spain

This paper presents a novel approach that exploits semantic knowledge to enhance the object recognition capability of autonomous robots. Semantic knowledge is a rich source of information, naturally gathered from humans (elicitation), which can encode both objects' geometrical/appearance properties and contextual relations. This kind of information can be exploited in a variety of robotics skills, especially for robots performing in human environments. In this paper we propose the use of semantic knowledge to eliminate the need of collecting large datasets for the training stages required in typical recognition approaches. Concretely, semantic knowledge encoded in an ontology is used to synthetically and effortless generate an arbitrary number of training samples for tuning probabilistic graphical models (PGMs). We then employ these PGMs to classify patches extracted from 3D point clouds gathered from office environments within the UMA-offices dataset, achieving a similar to 90% of recognition success, and from office and home scenes within the NYU2 dataset, yielding a success of similar to 81% and similar to 69.5% respectively. Additionally, a comparison with state-of-the-art recognition methods also based on graphical models has been carried out, revealing that our semantic-based training approach can compete with, and even outperform, those trained with a considerable number of real samples. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Semantic knowledge Human elicitation Object recognition probabilistic graphical models Autonomous robots

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An AI-based system for pricing diverse products and services

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KNOWLEDGE-BASED SYSTEMS 2010年第4期23卷 357-362页

作者： Shakya, S. Chin, C. M. Owusu, G. BT Innovate & Design Intelligent Syst Res Ctr Ipswich IP5 3RE Suffolk England

This paper describes an applied research work that looks at different ways to effectively manage resources Particularly, it describes how revenue management techniques can be used to balance demand against capacity, and describes a system that uses different OR and AI techniques to intelligently price diverse products and services This system can produce pricing policies for wide range of products and services regardless of the model of demand used The system incorporates a model specification layer, which provides flexibility in defining the demand model for different products It also incorporates an optimisation layer, which takes the specified model as an input and produces the pricing and production guidelines for the product The system can be either used as a stand alone system or can be incorporated as a generic modelling and optimisation component within a larger revenue management system (C) 2009 Elsevier B V All rights reserved

关键词： Estimation of distribution algorithms Genetic algorithm Evolutionary algorithm Evolutionary computation probabilistic graphical models Artificial intelligence

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Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2021年 131卷 151-188页

作者： Constantinou, Anthony C. Liu, Yang Chobtham, Kiattikun Guo, Zhigao Kitson, Neville K. Queen Mary Univ London QMUL Sch EECS Bayesian Artificial Intelligence Res Lab Risk & Informat Management RIM Res Grp London E1 4NS England Alan Turing Inst British Lib 96 Euston Rd London NW1 2DB England

Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across studies are often inconsistent in their claims about which algorithm is 'best'. This is partly because there is no agreed evaluation approach to determine their effectiveness. Moreover, each algorithm is based on a set of assumptions, such as complete data and causal sufficiency, and tend to be evaluated with data that conforms to these assumptions, however unrealistic these assumptions may be in the real world. As a result, it is widely accepted that synthetic performance overestimates real performance, although to what degree this may happen remains unknown. This paper investigates the performance of 15 state-of-the-art, well-established, or recent promising structure learning algorithms. We propose a methodology that applies the algorithms to data that incorporates synthetic noise, in an effort to better understand the performance of structure learning algorithms when applied to real data. Each algorithm is tested over multiple case studies, sample sizes, types of noise, and assessed with multiple evaluation criteria. This work involved learning approximately 10,000 graphs with a total structure learning runtime of seven months. In investigating the impact of data noise, we provide the first large scale empirical comparison of BN structure learning algorithms under different assumptions of data noise. The results suggest that traditional synthetic performance may overestimate real-world performance by anywhere between 10% and more than 50%. They also show that while score-based learning is generally superior to constraint-based learning, a higher fitting score does not necessarily imply a more accurate causal graph. The comparisons extend to other outcomes of interest, such as runtime, reliability, and resilience to nois

关键词： Ancestral graphs Causal discovery Causal insufficiency Directed acyclic graphs Measurement error probabilistic graphical models

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Affective Video Content Analysis via Multimodal Deep Quality Embedding Network

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2022年第3期13卷 1401-1415页

作者： Zhu, Yaochen Chen, Zhenzhong Wu, Feng Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430072 Hubei Peoples R China Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230052 Anhui Peoples R China

The establishment of large video affective content analysis datasets, such as LIRIS-ACCEDE, opens up the possibility of utilizing the massive representation power of deep neural networks (DNNs) to model the complex process of eliciting affective responses from video viewers. However, label noise in these datasets poses a considerable challenge to both the training and evaluation of DNNs. The optimization of DNNs requires stochastic gradient descent (SGD), but label noise in the training set leads to an inaccurate estimate of the gradient, which may cause the model to converge to a nonoptima. In addition, label noise in the test set renders the results of model evaluation untrustworthy. In this article, we propose a multimodal deep quality embedding network (MMDQEN) for affective video content analysis. Specifically, MMDQEN can infer the latent label and label quality from the noisy training samples so that cleaner supervision signals are provided to the DNN-based affective classifier, and a tractable objective for MMDQEN is derived with variational inference and conditional independence assumption. In addition, to avoid model evaluation bias incurred by the annotation noise in the test set, new test sets based on the original LIRIS-ACCEDE database, which we name LIRIS-ACCEDE-RANK, are established where the samples are ranked according to their label uncertainty level, with corresponding evaluation metrics introduced accordingly to further reveal the performance of different models. Experiments conducted on both the LIRIS-ACCEDE and the LIRIS-ACCEDE-RANK datasets demonstrate the effectiveness of the proposed method.

关键词： Annotations Feature extraction Training Neural networks probabilistic logic Noise measurement Color Affective video content analysis label noise probabilistic graphical models variational inference deep neural networks

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Modelling spatial and temporal changes with GIS and Spatial and Dynamic Bayesian Networks

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ENVIRONMENTAL MODELLING & SOFTWARE 2016年 82卷 108-120页

作者： Chee, Yung En Wilkinson, Lauchlin Nicholson, Ann E. Quintana-Ascencio, Pedro F. Fauth, John E. Hall, Dianne Ponzio, Kimberli J. Rumpff, Libby Univ Melbourne Sch Biosci Parkville Vic 3010 Australia Univ Melbourne Sch Ecosyst & Forest Sci 500 Yarra Blvd Burnley Vic 3121 Australia Monash Univ Fac Informat Technol POB 197 Caulfield Vic 3145 Australia Univ Cent Florida Dept Biol 4000 Cent Florida Blvd Orlando FL 32816 USA St Johns River Water Management Dist Div Water Resources POB 1429 Palatka FL 32178 USA

State-and-transition models (STMs) have been successfully combined with Dynamic Bayesian Networks (DBNs) to model temporal changes in managed ecosystems. Such models are useful for exploring when and how to intervene to achieve the desired management outcomes. However, knowing where to intervene is often equally critical. We describe an approach to extend state-and-transition dynamic Bayesian networks (ST-DBNs) - incorporating spatial context via GIS data and explicitly modelling spatial processes using spatial Bayesian networks (SBNs). Our approach uses object-oriented (OO) concepts and exploits the fact that ecological systems are hierarchically structured: This allows key phenomena and ecological processes to be represented by hierarchies of components that include similar, repetitive structures. We demonstrate the generality and power of our approach using two models - one developed for adaptive management of eucalypt woodland restoration in south-eastern Australia, and another developed to manage the encroachment of invasive willows into marsh ecosystems in east central Florida. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： probabilistic graphical models State-and-transition models Object-oriented Adaptive management Willow

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Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 56卷 197-208页

作者： Constantinou, Anthony Costa Fenton, Norman Neil, Martin Queen Mary Univ London Sch Elect Engn & Comp Sci Risk & Informat Management RIM Res Grp London E1 4NS England Agena Ltd Cambridge CB23 7NU England

When developing a causal probabilistic model, i.e. a Bayesian network (BN), it is common to incorporate expert knowledge of factors that are important for decision analysis but where historical data are unavailable or difficult to obtain. This paper focuses on the problem whereby the distribution of some continuous variable in a BN is known from data, but where we wish to explicitly model the impact of some additional expert variable (for which there is expert judgment but no data). Because the statistical outcomes are already influenced by the causes an expert might identify as variables missing from the dataset, the incentive here is to add the expert factor to the model in such a way that the distribution of the data variable is preserved when the expert factor remains unobserved. We provide a method for eliciting expert judgment that ensures the expected values of a data variable are preserved under all the known conditions. We show that it is generally neither possible, nor realistic, to preserve the variance of the data variable, but we provide a method towards determining the accuracy of expertise in terms of the extent to which the variability of the revised empirical distribution is minimised. We also describe how to incorporate the assessment of extremely rare or previously unobserved events. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Bayesian networks Belief networks Causal inference Expert knowledge Knowledge elicitation probabilistic graphical models

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