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Multiple-Object Tracking in Large-Scale Scene

IEICE TRANSACTIONS ON INFORMATION AND systems 2016年第7期E99D卷 1903-1909页

作者： Yuan, Wenbo Cao, Zhiqiang Tan, Min Chen, Hongkai Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China

In this paper, a multiple-object tracking approach in large-scale scene is proposed based on visual sensor network. Firstly, the object detection is carried out by extracting the HOG features. Then, object tracking is performed based on an improved particle filter method. On the one hand, a kind of temporal and spatial dynamic model is designed to improve the tracking precision. On the other hand, the cumulative error generated from evaluating particles is eliminated through an appearance model. In addition, losses of the tracking will be incurred for several reasons, such as occlusion, scene switching and leaving. When the object is in the scene under monitoring by visual sensor network again, object tracking will continue through object re-identification. Finally, continuous multiple-object tracking in large-scale scene is implemented. A database is established by collecting data through the visual sensor network. Then the performances of object tracking and object re-identification are tested. The effectiveness of the proposed multiple-object tracking approach is verified.

关键词： visual sensor network HOG improved particle filter re-identification object tracking

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Improving invariance in visual classification with biologically inspired mechanism

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NEUROCOMPUTING 2014年 133卷 328-341页

作者： Tang, Tang Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

A computational model of visual cortex has raised great interest in developing algorithms mimicking human visual systems. The max-operation is employed in the model to emulate the scale and position invariant responses of the visual cells. We further extend this idea to enhance the tolerance of visual classification against the general intra-class variability. A general architecture of the basic block constituting the model is first presented. The architecture adaptively chooses the best matching template from a set of competing templates to predict the label of the incoming sample. To optimize the non-convex and non-smooth objective function resulted, we develop an algorithm to train each template alternately. Experiments show that the proposed method significantly outperforms linear classifiers as a template matching method in several image classification tasks, and is much more computationally efficient than other commonly used non-linear classifiers. In the image classification task on the Caltech 101 database, the performance of the biologically inspired model is obviously boosted by incorporating the proposed method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Biologically inspired Visual classification Max-pooling Template matching

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A data-based online reinforcement learning algorithm satisfying probably approximately correct principle

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NEURAL COMPUTING & APPLICATIONS 2015年第4期26卷 775-787页

作者： Zhu, Yuanheng Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

This paper proposes a probably approximately correct (PAC) algorithm that directly utilizes online data efficiently to solve the optimal control problem of continuous deterministic systems without system parameters for the first time. The dependence on some specific approximation structures is crucial to limit the wide application of online reinforcement learning (RL) algorithms. We utilize the online data directly with the kd-tree technique to remove this limitation. Moreover, we design the algorithm in the PAC principle. Complete theoretical proofs are presented, and three examples are simulated to verify its good performance. It draws the conclusion that the proposed RL algorithm specifies the maximum running time to reach a near-optimal control policy with only online data.

关键词： Reinforcement learning Probably approximately correct Kd-tree

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Identification for Switched systems

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IFAC-PapersOnLine 2015年第28期48卷 514-519页

作者： Hu, Qiong Fei, Qing Ma, Hongbin Wu, Qinghe Geng, Qingbo State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

For a class of unstable discrete-time multi-variable switched systems with parametric uncertainty, an identification scheme is developed in this paper. Specifically, the identification algorithm is derived to identify the parameter vectors of potential subsystems based on the idea of least geometric mean squares. Furthermore, an neural network classifier is put forward for partition of the identified subsystems. Finally, feasibility and effectiveness of the proposed identification scheme are verified by numerical simulations, which show that the desirable identification performance is guaranteed with the proposed scheme. © 2015

关键词： Parameter estimation Computational fluid dynamics Geometric mean Identification algorithms Identification scheme Multi variables Neural network classifier Parameter vectors Parametric uncertainties Switched system

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Point-Manifold Discriminant Analysis for Still-to-Video Face Recognition

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IEICE TRANSACTIONS ON INFORMATION AND systems 2014年第10期E97D卷 2780-2789页

作者： Chen, Xue Wang, Chunheng Xiao, Baihua Shao, Yunxue Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China

In Still-to-Video (S2V) face recognition, only a few high resolution images are registered for each subject, while the probe is video clips of complex variations. As faces present distinct characteristics under different scenarios, recognition in the original space is obviously inefficient. Thus, in this paper, we propose a novel discriminant analysis method to learn separate mappings for different scenario patterns (still, video), and further pursue a common discriminant space based on these mappings. Concretely, by modeling each video as a manifold and each image as point data, we form the scenario-oriented mapping learning as a Point-Manifold Discriminant Analysis (PMDA) framework. The learning objective is formulated by incorporating the intra-class compactness and inter-class separability for good discrimination. Experiments on the COX-S2V dataset demonstrate the effectiveness of the proposed method.

关键词： face recognition still-to-video discriminant analysis point-manifold distance scenario-oriented

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The DAO to Metacontrol for Metasystems in Metaverses:The System of Parallel control systems for Knowledge Automation and control Intelligence in CPSS

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1899-1908页

作者： Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing100190Chi the Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

An investigation and outline of Metacontrol and Decontrol in Metaverses for control intelligence and knowledge automation are *** control with prescriptive knowledge and parallel philosophy is proposed as the starting point for the new control philosophy and technology,especially for computational control of metasystems in cyberphysical-social *** argue that circular causality,the generalized feedback mechanism for complex and purposive systems,should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in ***,an interdisciplinary approach is suggested for Metacontrol and Decontrol as a new form of intelligent control based on five control metaverses:MetaVerses,MultiVerses,InterVerses,TransVerse,and DeepVerses.

关键词： Metacontrol Metasystems Metaverses CPSS DAO Decontrol Prescriptive control Parallel control Computational control Knowledge Automation control Intelligence Parallel Intelligence

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Social influence and spread dynamics in social networks

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Frontiers of Computer Science 2012年第5期6卷 611-620页

作者： Xiaolong ZHENG Yongguang ZHONG Daniel ZENG Fei-Yue WANG State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Management Science and Engineering Qingdao UniversityQingdao 266071China

Social networks often serve as a critical medium for information dissemination, diffusion of epidemics, and spread of behavior, by shared activities or similarities be- tween individuals. Recently, we have witnessed an explosion of interest in studying social influence and spread dynamics in social networks. To date, relatively little material has been provided on a comprehensive review in this field. This brief survey addresses this issue. We present the current significant empirical studies on real social systems, including network construction methods, measures of network, and newly em- pirical results. We then provide a concise description of some related social models from both macro- and micro-level per- spectives. Due to the difficulties in combining real data and simulation data for verifying and validating real social sys- tems, we further emphasize the current research results of computational experiments. We hope this paper can provide researchers significant insights into better understanding the characteristics of personal influence and spread patterns in large-scale social systems.

关键词： social networks spread dynamics social influence computational experiment

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Generative Adversarial Networks:Introduction and Outlook

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 588-598页

作者： Kunfeng Wang Chao Gou Yanjie Duan Yilun Lin Xinhu Zheng Fei-Yue Wang IEEE State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences Department of Computer Science and Engineering University of Minnesota Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology

Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning *** goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that *** their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application ***, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

关键词： ACP approach adversarial learning generative adversarial networks(GANs) generative models parallel intelligence zero-sum game

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controlling the depth of a gliding robotic dolphin using dual motion control modes

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Science China(Information Sciences) 2020年第9期63卷 217-230页

作者： Jian WANG Zhengxing WU Min TAN Junzhi YU State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESAT College of Engineering Peking University

This paper investigates the performance of the dual mode, namely flipper mode and central pattern generator(CPG) mode, for controlling the depth of a gliding robotic dolphin. Subsequent to considering the errors in dynamic models, we propose a depth control system that combines the line-of-sight(LOS)method with an adaptive control approach(ACA) to deal with uncertainties in the model parameters. First,we establish a full-state dynamic model to conduct simulations and optimize the parameters used in later aquatic experiments. Then, we use the LOS method to transform the control target from the depth to the pitch angle and employ the ACA to calculate the control signal. In particular, we optimize the ACA’s control parameters using simulations based on our dynamic model. Finally, our simulated and experimental results demonstrate not only that we can successfully control the robotic dolphin’s depth, but also that its performance was better than that of the CPG-based control, thus indicating that we can achieve three-dimensional motion by combining flipper-based and CPG-based control. The results of this study suggest valuable ideas for practical applications of gliding robotic dolphins.

关键词： gliding robotic dolphin depth control dual motion adaptive control approach

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Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1210-1220页

作者： Guojun Wang Jian Wu Rui He Bin Tian State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the Qingdao Academy of Intelligent Industries ShandongChina

Road boundary detection is essential for autonomous vehicle localization and decision-making,especially under GPS signal loss and lane *** road boundary detection in structural environments,obstacle occlusions and large road curvature are two significant ***,an effective and fast solution for these problems has remained *** solve these problems,a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is *** proposed method consists of three main stages:1)a multi-feature based method is applied to extract feature points;2)a road-segmentation-line-based method is proposed for classifying left and right feature points;3)an iterative Gaussian Process Regression(GPR)is employed for filtering out false points and extracting boundary *** demonstrate the effectiveness of the proposed method,KITTI datasets is used for comprehensive experiments,and the performance of our approach is tested under different road *** experiments show the roadsegmentation-line-based method can classify left,and right feature points on structured curved roads,and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic ***,the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.

关键词： 3D-LiDAR autonomous vehicle object detection point cloud road boundary

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