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Human action recognition using weighted pooling

IET COMPUTER VISION 2014年第6期8卷 579-587页

作者： Zhou, Wen Wang, Chunheng Xiao, Baihua Zhang, Zhong CASIA State Key Lab Management & Control Complex Syst Beijing Peoples R China

Pooling strategies, such as max pooling and sum pooling, have been widely used to obtain the global representations for action videos. However, these pooling strategies have several disadvantages. First, they are easily affected by unwanted background local features, the absence of discriminative local features and the times of actions periodically performed by actors. Second, most pooling strategies only use local features to build the global representation that captures little mid-level features for action representation. In this study, the authors propose a novel weighted pooling strategy based on actionlets representation for action recognition. The actionlets are defined as the movements of large bodies such as legs, arms and head, which capture rich mid-level features for action representation. Besides, the authors' method also incorporates the distribution information of actionlets into pooling procedure. Specifically, a pooling weight, which determines the importance of actionlet on the final video representation, is assigned to each actionlet. To learn the weight, they propose a novel discriminative learning algorithm to capture the discriminative information for pooling operation. They evaluate their weighted pooling on three datasets: KTH actions dataset, UCF sports dataset and Youtube actions dataset. Experimental results show the effectiveness of the proposed method.

关键词： image representation learning (artificial intelligence) gesture recognition human action recognition pooling strategies max pooling sum pooling background local features discriminative local features global representation action representation weighted pooling strategy actionlets representation actionlets distribution information pooling procedure video representation discriminative learning algorithm pooling operation UCF sports dataset Youtube actions dataset KTH actions dataset

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Containment control of General Linear Multi-agent systems with Multiple Dynamic Leaders:a Fast Sliding Mode Based Approach

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IEEE/CAA Journal of Automatica Sinica 2014年第2期1卷 134-140页

作者： Huiyang Liu Long Cheng Min Tan Zengguang Hou State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

In this paper, distributed containment control problems of general linear multi-agent systems are investigated. The objective is to make the followers in a multi-agent network converge to the convex hull spanned by some leaders whose control inputs are nonzero and not available to any *** mode surfaces are defined for the cases of reduced order and non-reduced order, respectively. For each case, fast sliding mode controllers are designed. It is shown that all the error trajectories exponentially reach the sliding mode surfaces in a finite time if for each follower, there exists at least one of the leaders who has a directed path to the follower, and the leaderscontrol inputs are bounded. The control Lyapunov function for exponential finite time stability, motivated by the fast terminal sliding mode control, is used to prove reachability of the sliding mode surfaces. Simulation examples are given to illustrate the theoretical results.

关键词： Multi-agent systems Sliding mode control Trajectory Robot sensing systems Robot kinematics Manifolds

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Salient Local Binary Pattern for Ground-Based Cloud Classification

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Acta meteorologica Sinica 2013年第2期27卷 211-220页

作者：刘爽王春恒肖柏华张重邵允学 State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern （SLBP）, is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns （the salient patterns） to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.

关键词： salient local binary pattern local binary pattern ground-based cloud classification

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What will robots be like in the future?

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National Science Review 2019年第5期6卷 1059-1061页

作者： Yanfeng Lu Weijie Zhao the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences NSR

Robots are changing our lives:sweeping robots patrol our living rooms; interactive robots accompany our children; industrial robots assemble vehicles; rescue robots search and save lives in catastrophes; medical robots perform surgeries in *** better understand robots’challenges and impact, National Science Review (NSR) interviewed Professor Toshio Fukuda, who is one of the world’s most representative robotics experts and has developed a number of bionic robots and micro/nano-robots.

关键词： NSR China What will robots be like in the future

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A Novel Data-Based Fault-Tolerant control Method for Multicontroller Linear systems via Distributed Policy Iteration

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第5期53卷 3176-3186页

作者： Wei, Qinglai Li, Hongyang Li, Tao Wang, Fei-Yue Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China

This article presents a novel data-based fault-tolerant control method for multicontroller linear systems via distributed policy iteration. The traditional fault-tolerant control methods based on policy iteration may cause a huge-computational burden under the situation of high-dimension control laws. In order to solve this problem, a novel distributed policy iteration method is presented, where only one iterative control law is updated in each iteration, to realize the fault-tolerant control of multicontroller linear systems. The main contributions can be highlighted as follows: 1) a novel data-based distributed policy iteration method is presented to reduce the computational burden;2) the fault-tolerant control method is presented via designing fault compensators;and 3) the developed data-based method only requires the partial system information. First, the model-based fault-tolerant control via distributed policy iteration and fault compensation is provided. Based on the model-based method, a data-based fault-tolerant control method is presented. Finally, numerical experiments are given to show the performance of the presented method.

关键词： Fault tolerant systems Fault tolerance Linear systems control systems Actuators Optimal control Mathematical models Distributed policy iteration fault-tolerant control multicontroller linear systems optimal control

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A Neural-Network-Based controller for Piezoelectric-Actuated Stick-Slip Devices

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2598-2607页

作者： Cheng, Long Liu, Weichuan Yang, Chenguang Huang, Tingwen Hou, Zeng-Guang Tan, Min State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Zienkiewicz Centre for Computational Engineering Swansea University Swansea U.K. Texas A&M University at Qatar Doha Qatar State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Piezoelectric-actuated stick-slip device (PASSD) is a highly promising equipment that is composed of one end-effector, one piezoelectric actuator (PEA) and one driving object adhered to the PEA. Since the endeffector can slip on the surface of the driving object, the PASSD is capable of realizing the macrolevel motion with the microlevel precision. Due to the following two reasons: the complicated relative motion between the end-effector and the driving object, and the inherent hysteresis nonlinearity in the PEA, the ultraprecision displacement control of the end-effector of PASSDs raises a real challenge, which is rarely reported in the literature. Toward solving this challenge, a neural-network-based controller is proposed in this paper. First, a neural-network-based model is proposed to capture the relative motion between the end-effector and the driving object. Second, a neural-network-based inversion model is developed to online calculate the desired position of the PEA under the predesigned reference of the end-effector. Third, a dynamic linearized neural-network-based model predictive control method, which can effectively handle the hysteresis nonlinearity, is employed to implement the displacement control of the PEA, which finally results in an overall high-precision controller of the end-effector. Finally, a PASSD prototype has been implemented and tested through experimental studies to demonstrate the effectiveness of the proposed approach.

关键词： End-effector neural network piezoelectric actuator (PEA) stick-slip

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Towards an Esox lucius inspired multimodal robotic fish

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Science China(Information Sciences) 2015年第5期58卷 182-194页

作者： WU ZhengXing YU JunZhi SU ZongShuai TAN Min LI ZhenLong State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Electronic Information and Control Engineering Beijing University of Technology

In this paper, we further explore multimodal locomotion via an updated robotic fish model based on Esox lucius. Besides the improved actuation properties like higher torque servomotors and powerful electronics,the robotic fish has some innovative mechanical design to pursue diverse swimming modes and superior performance. Specifically, we introduced a ±50° yawing head joint that functions as the neck for enhancing turning ability. A pair of pectoral mechanisms with two DOFs per fin is constructed to achieve 3-D swimming and to enrich multiple pectoral motions. At the control level, an improved central pattern generator(CPG) model allowing for free adjustment of the phase relationship among outputs is employed to produce rhythmic signals of multimodal swimming. Extensive experiments were carried out to examine how characteristic parameters in CPGs including amplitude, frequency, and phase lag affect the swimming performance. As a result, the robotic fish successfully performed various locomotion actions such as forward swimming, backward swimming, turning, diving, surfacing, as well as three pectoral motions in the form of pitching, heaving, and *** found that small phase lag between oscillating joints which means large propulsive body wave length and undulation width could lead to a faster swimming in body and/or caudal fin(BCF) locomotion.

关键词： bio-inspired robot robotic fish CPG multimodal swimming motion control

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Design and attitude control of a novel robotic jellyfish capable of 3D motion

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Science China(Information Sciences) 2019年第9期62卷 182-184页

作者： Junzhi YU Xiangbin LI Lei PANG Zhengxing WU State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

Aquatic creatures such as fish, cetaceans, and jellyfish could inspire innovative designs to improve the ways that manmade systems operate in and interact with aquatic environments [1, 2]. Jellyfish in nature use jet propulsion to move through the water, which have been proven to be one of the most energetically efficient swimmers on the planet [3]. Researchers are making an integrated effort to develop smart actuators to fabricate var-

关键词： bar Design and attitude control of a novel robotic jellyfish capable of 3D motion

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Scene Text Recognition Using Structure-Guided Character Detection and Linguistic Knowledge

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2014年第7期24卷 1235-1250页

作者： Shi, Cun-Zhao Wang, Chun-Heng Xiao, Bai-Hua Gao, Song Hu, Jin-Long CASIA State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Scene text recognition has inspired great interests from the computer vision community in recent years. In this paper, we propose a novel scene text-recognition method integrating structure-guided character detection and linguistic knowledge. We use part-based tree structure to model each category of characters so as to detect and recognize characters simultaneously. Since the character models make use of both the local appearance and global structure informations, the detection results are more reliable. For word recognition, we combine the detection scores and language model into the posterior probability of character sequence from the Bayesian decision view. The final word-recognition result is obtained by maximizing the character sequence posterior probability via Viterbi algorithm. Experimental results on a range of challenging public data sets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method achieves state-of-the-art performance both for character detection and word recognition.

关键词： Character recognition cropped word recognition part-based tree-structured models (TSMs) posterior probability scene text recognition word spotting

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Action Recognition Using Context-Constrained Linear Coding

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IEEE SIGNAL PROCESSING LETTERS 2012年第7期19卷 439-442页

作者： Zhang, Zhong Wang, Chunheng Xiao, Baihua Zhou, Wen Liu, Shuang CASIA State Key Lab Management & Control Complex Syst Beijing Peoples R China

Although traditional bag-of-words model has shown promising results for action recognition, it takes no consideration of the relationship among spatio-temporal points;furthermore, it also suffers serious quantization error. In this letter, we propose a novel coding strategy called context-constrained linear coding (CLC) to overcome these limitations. We first calculate the contextual distance between local descriptors and each codeword by considering the spatio-temporal contextual information. Then, linear coding using contextual distance is adopted to alleviate the quantization error. Our method is verified on two challenging databases (KTH and UCF sports), and the experimental results demonstrate that our method achieves better results than previous methods in action recognition.

关键词： Action recognition context-constrained linear coding contextual distance

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