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Video Anomaly Detection Using Ensemble One-Class Classifiers Scopus
会议论文 | 2018 , 2018-July , 9343-9349
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Abstract :

© 2018 Technical Committee on Control Theory, Chinese Association of Automation. In this paper we present a novel algorithm for video anomaly detection. It is based on multiple local cells, which are acquired by splitting entire monitor scene. At each local cell, we group all feature vectors with clustering algorithm based on minimum spanning tree, and further model all groups using improved one-class SVM to build ensemble classifiers. For any new features at each local node in incoming video clips, we use the corresponding learned ensemble classifiers to estimate maximum abnormality degree. The proposed approach has been tested on publicly available datasets with frame-level and pixel-level criteria, and outperforms other state-of-the-art approaches.

Keyword :

Anomaly detection Clustering algorithm Minimum spanning tree One-class classifier

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GB/T 7714 Li, Gang , Feng, Zuren , Lv, Na . Video Anomaly Detection Using Ensemble One-Class Classifiers [C] . 2018 : 9343-9349 .
MLA Li, Gang 等. "Video Anomaly Detection Using Ensemble One-Class Classifiers" . (2018) : 9343-9349 .
APA Li, Gang , Feng, Zuren , Lv, Na . Video Anomaly Detection Using Ensemble One-Class Classifiers . (2018) : 9343-9349 .
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Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range EI SCIE PubMed Scopus
期刊论文 | 2018 , 18 (2) | SENSORS
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Abstract :

For Wireless Sensor Networks (WSNs), the Voronoi partition of a region is a challenging problem owing to the limited sensing ability of each sensor and the distributed organization of the network. In this paper, an algorithm is proposed for each sensor having a limited sensing range to compute its limited Voronoi cell autonomously, so that the limited Voronoi partition of the entire WSN is generated in a distributed manner. Inspired by Graham's Scan (GS) algorithm used to compute the convex hull of a point set, the limited Voronoi cell of each sensor is obtained by sequentially scanning two consecutive bisectors between the sensor and its neighbors. The proposed algorithm called the Boundary Scan (BS) algorithm has a lower computational complexity than the existing Range-Constrained Voronoi Cell (RCVC) algorithm and reaches the lower bound of the computational complexity of the algorithms used to solve the problem of this kind. Moreover, it also improves the time efficiency of a key step in the Adjust-Sensing-Radius (ASR) algorithm used to compute the exact Voronoi cell. Extensive numerical simulations are performed to demonstrate the correctness and effectiveness of the BS algorithm. The distributed realization of the BS combined with a localization algorithm in WSNs is used to justify the WSN nature of the proposed algorithm.

Keyword :

distributed algorithm local information sensing capability Wireless Sensor Networks (WSNs) limited Voronoi partition

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GB/T 7714 He, Chenlong , Feng, Zuren , Ren, Zhigang . Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range [J]. | SENSORS , 2018 , 18 (2) .
MLA He, Chenlong 等. "Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range" . | SENSORS 18 . 2 (2018) .
APA He, Chenlong , Feng, Zuren , Ren, Zhigang . Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range . | SENSORS , 2018 , 18 (2) .
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An Improved Randomized Local Binary Features for Keypoints Recognition EI SCIE PubMed Scopus
期刊论文 | 2018 , 18 (6) | SENSORS
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Abstract :

In this paper, we carry out researches on randomized local binary features. Randomized local binary features have been used in many methods like RandomForests, RandomFerns, BRIEF, ORB and AKAZE to matching keypoints. However, in those existing methods, the randomness of feature operators only reflects in sampling position. In this paper, we find the quality of the binary feature space can be greatly improved by increasing the randomness of the basic sampling operator. The key idea of our method is to use a Randomized Intensity Difference operator (we call it RID operator) as a basic sampling operator to observe image patches. The randomness of RID operators are reflected in five aspects: grids, position, aperture, weights and channels. Comparing with the traditional incompletely randomized binary features (we call them RIT features), a completely randomized sampling manner can generate higher quality binary feature space. The RID operator can be used on both gray and color images. We embed different kinds of RID operators into RandomFerns and RandomForests classifiers to test their recognition rate on both image and video datasets. The experiment results show the excellent quality of our feature method. We also propose the evaluation criteria for robustness and distinctiveness to observe the effects of randomization on binary feature space.

Keyword :

keypoints recognition random ferns random forests binary feature SIFT ORB

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GB/T 7714 Zhang, Jinming , Feng, Zuren , Zhang, Jinpeng et al. An Improved Randomized Local Binary Features for Keypoints Recognition [J]. | SENSORS , 2018 , 18 (6) .
MLA Zhang, Jinming et al. "An Improved Randomized Local Binary Features for Keypoints Recognition" . | SENSORS 18 . 6 (2018) .
APA Zhang, Jinming , Feng, Zuren , Zhang, Jinpeng , Li, Gang . An Improved Randomized Local Binary Features for Keypoints Recognition . | SENSORS , 2018 , 18 (6) .
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Community Detection Using Dual-Representation Chemical Reaction Optimization SCIE
期刊论文 | 2017 , 47 (12) , 4328-4341 | IEEE TRANSACTIONS ON CYBERNETICS | IF: 8.803
WoS CC Cited Count: 2
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Abstract :

Many complex networks have been shown to have community structures. Detecting those structures is very important for understanding the organization and function of networks. Because this problem is NP-hard, it is appropriate to resort to evolutionary algorithms. Chemical reaction optimization (CRO) is a novel evolutionary algorithm inspired by the interactions among molecules during chemical reactions. In this paper, we propose a CRO variant named dual-representation CRO (DCRO) to address the community detection problem. DCRO encodes a solution in two representations: one is locus-based and the other is vector-based. The former representation can ensure the validity of a solution and fits for diversification search, and the latter is convenient for intensification search. We thus design two operators for CRO based on these two representations. Their cooperation enables DCRO to achieve a good balance between exploration and exploitation. Experimental results on synthetic and real-life networks show that DCRO can find community structures close to the actual ones and is capable of achieving solutions comparable to several state-of-the-art methods.

Keyword :

community detection metaheuristic complex network evolutionary computation Chemical reaction optimization (CRO)

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GB/T 7714 Chang, Honghao , Feng, Zuren , Ren, Zhigang . Community Detection Using Dual-Representation Chemical Reaction Optimization [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2017 , 47 (12) : 4328-4341 .
MLA Chang, Honghao et al. "Community Detection Using Dual-Representation Chemical Reaction Optimization" . | IEEE TRANSACTIONS ON CYBERNETICS 47 . 12 (2017) : 4328-4341 .
APA Chang, Honghao , Feng, Zuren , Ren, Zhigang . Community Detection Using Dual-Representation Chemical Reaction Optimization . | IEEE TRANSACTIONS ON CYBERNETICS , 2017 , 47 (12) , 4328-4341 .
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Asynchronous Motor Imagery Detection Based on a Target Guided Sub-band Filter Using Wavelet Packets EI CPCI-S Scopus
会议论文 | 2017 , 4850-4855 | 29th Chinese Control And Decision Conference (CCDC)
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Abstract :

For an asynchronous system based on brain-computer interface (BCI), detecting the occurrence of motor imagery by electroencephalogram (EEG) signals is the basis but also a challenge, due to the complex and non-stationary characteristics of EEG signals. This paper employs a filtering method which uses a the target guided sub-band filter combined with an energy detector for asynchronous motor imagery detection. The proposed filter in the wavelet packet transform domain uses a prior knowledge of the motor imagery and also applies the idea of background suppressing. It can pass the frequency bands that are more significant in the motor imagery signal than in the noise. Experiment demonstrated that the proposed method was reliable for practical use with an equal error rate (EER) of about 9% and a mean response time of 4.36s.

Keyword :

Motor Imagery Asynchronous Wavelet Packet Transform Brain-computer Interface

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GB/T 7714 Sun, Yujuan , Feng, Zuren , Zhang, Jun et al. Asynchronous Motor Imagery Detection Based on a Target Guided Sub-band Filter Using Wavelet Packets [C] . 2017 : 4850-4855 .
MLA Sun, Yujuan et al. "Asynchronous Motor Imagery Detection Based on a Target Guided Sub-band Filter Using Wavelet Packets" . (2017) : 4850-4855 .
APA Sun, Yujuan , Feng, Zuren , Zhang, Jun , Zhou, Qing , Luo, Jing . Asynchronous Motor Imagery Detection Based on a Target Guided Sub-band Filter Using Wavelet Packets . (2017) : 4850-4855 .
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Object Tracking Using Local Multiple Features and a Posterior Probability Measure EI SCIE PubMed Scopus
期刊论文 | 2017 , 17 (4) | SENSORS | IF: 2.475
SCOPUS Cited Count: 1
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Abstract :

Object tracking has remained a challenging problem in recent years. Most of the trackers can not work well, especially when dealing with problems such as similarly colored backgrounds, object occlusions, low illumination, or sudden illumination changes in real scenes. A centroid iteration algorithm using multiple features and a posterior probability criterion is presented to solve these problems. The model representation of the object and the similarity measure are two key factors that greatly influence the performance of the tracker. Firstly, this paper propose using a local texture feature which is a generalization of the local binary pattern (LBP) descriptor, which we call the double center-symmetric local binary pattern (DCS-LBP). This feature shows great discrimination between similar regions and high robustness to noise. By analyzing DCS-LBP patterns, a simplified DCS-LBP is used to improve the object texture model called the SDCS-LBP. The SDCS-LBP is able to describe the primitive structural information of the local image such as edges and corners. Then, the SDCS-LBP and the color are combined to generate the multiple features as the target model. Secondly, a posterior probability measure is introduced to reduce the rate of matching mistakes. Three strategies of target model update are employed. Experimental results show that our proposed algorithm is effective in improving tracking performance in complicated real scenarios compared with some state-of-the-art methods.

Keyword :

multiple features object tracking centroid iteration posterior probability measure

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GB/T 7714 Guo, Wenhua , Feng, Zuren , Ren, Xiaodong . Object Tracking Using Local Multiple Features and a Posterior Probability Measure [J]. | SENSORS , 2017 , 17 (4) .
MLA Guo, Wenhua et al. "Object Tracking Using Local Multiple Features and a Posterior Probability Measure" . | SENSORS 17 . 4 (2017) .
APA Guo, Wenhua , Feng, Zuren , Ren, Xiaodong . Object Tracking Using Local Multiple Features and a Posterior Probability Measure . | SENSORS , 2017 , 17 (4) .
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Feature Extraction by Common Spatial Pattern in Frequency Domain for Motor Imagery Tasks Classification EI CPCI-S Scopus
会议论文 | 2017 , 5883-5888 | 29th Chinese Control And Decision Conference (CCDC)
WoS CC Cited Count: 3 SCOPUS Cited Count: 5
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Abstract :

Common spatial pattern (CSP) as a feature extraction algorithm has been successfully applied to classify EEG based motor imagery tasks in brain computer interface (BCI). Successful application of CSP depends on the character of input signals and the first and last m eigenvectors of projection matrix. In this study, we proposed a novel and robust feature extraction method designated frequency domain CSP (FDCSP) that the samples in frequency domain obtained by fast Fourier transform (FFT) algorithm and evenly distributed in 8-30Hz were employed as the input signals of CSP. Besides, we made some modifications to classical CSP to address the inconsistent issue and enhance the generalization ability. Cross validation classification accuracy and standard deviation based on training data were employed as the principle to optimize the subject-specific parameter m. Two public EEG datasets (BCI competition IV dataset 2a and 2b) were used to validate the proposed method. Experimental results demonstrated that the proposed method significantly outperformed many other state-of-the-art methods in classification performance. What's more, samples in frequency domain as the input signals of CSP are demonstrated more robust against preprocessing. Based on the two public datasets, the proposed FDCSP method has potential significance to motor imagery based BCI design in practice.

Keyword :

Motor Imagery Tasks Classification Frequency Domain Samples Common Spatial Pattern (CSP)

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GB/T 7714 Wang, Jie , Feng, Zuren , Lu, Na . Feature Extraction by Common Spatial Pattern in Frequency Domain for Motor Imagery Tasks Classification [C] . 2017 : 5883-5888 .
MLA Wang, Jie et al. "Feature Extraction by Common Spatial Pattern in Frequency Domain for Motor Imagery Tasks Classification" . (2017) : 5883-5888 .
APA Wang, Jie , Feng, Zuren , Lu, Na . Feature Extraction by Common Spatial Pattern in Frequency Domain for Motor Imagery Tasks Classification . (2017) : 5883-5888 .
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Comprehensive Pareto Efficiency in robust counterpart optimization EI SCIE Scopus
期刊论文 | 2016 , 94 , 75-91 | COMPUTERS & CHEMICAL ENGINEERING | IF: 3.024
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In this paper, an innovative concept named Comprehensive Pareto Efficiency is introduced in the context of robust counterpart optimization, which consists of three sub-concepts: Pareto Robust Optimality (PRO), Global Pareto Robust Optimality (GPRO) and Elite Pareto Robust Optimality (EPRO). Different algorithms are developed for computing robust solutions with respect to these three sub-concepts. As all sub-concepts are based on the Probability of Constraint Violation (PCV), formulations of PCV under different probability distributions are derived and an alternative way to calculate PCV is also presented. Numerical studies are drawn from two applications (production planning problem and orienteering problem), to demonstrate the Comprehensive Pareto Efficiency. The numerical results show that the Comprehensive Pareto Efficiency has important significance for practical applications in terms of the evaluation of the quality of robust solutions and the analysis of the difference between different robust counterparts, which provides a new perspective for robust counterpart optimization. (C) 2016 Elsevier Ltd. All rights reserved.

Keyword :

Robust optimization Linear programming Pareto optimality Integer programming

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GB/T 7714 Shang, Ke , Feng, Zuren , Ke, Liangjun et al. Comprehensive Pareto Efficiency in robust counterpart optimization [J]. | COMPUTERS & CHEMICAL ENGINEERING , 2016 , 94 : 75-91 .
MLA Shang, Ke et al. "Comprehensive Pareto Efficiency in robust counterpart optimization" . | COMPUTERS & CHEMICAL ENGINEERING 94 (2016) : 75-91 .
APA Shang, Ke , Feng, Zuren , Ke, Liangjun , Chan, Felix T. S. . Comprehensive Pareto Efficiency in robust counterpart optimization . | COMPUTERS & CHEMICAL ENGINEERING , 2016 , 94 , 75-91 .
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Unfalsified Visual Servoing for Simultaneous Object Recognition and Pose Tracking EI SCIE Scopus
期刊论文 | 2016 , 46 (12) , 3032-3046 | IEEE Transactions on Cybernetics | IF: 7.384
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Abstract :

In a complex environment, simultaneous object recognition and tracking has been one of the challenging topics in computer vision and robotics. Current approaches are usually fragile due to spurious feature matching and local convergence for pose determination. Once a failure happens, these approaches lack a mechanism to recover automatically. In this paper, data-driven unfalsified control is proposed for solving this problem in visual servoing. It recognizes a target through matching image features with a 3-D model and then tracks them through dynamic visual servoing. The features can be falsified or unfalsified by a supervisory mechanism according to their tracking performance. Supervisory visual servoing is repeated until a consensus between the model and the selected features is reached, so that model recognition and object tracking are accomplished. Experiments show the effectiveness and robustness of the proposed algorithm to deal with matching and tracking failures caused by various disturbances, such as fast motion, occlusions, and illumination variation.

Keyword :

visual tracking Object recognition unfalsified adaptive control visual servoing supervisory control

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GB/T 7714 Jiang, Ping , Cheng, Yongqiang , Wang, Xiaonian et al. Unfalsified Visual Servoing for Simultaneous Object Recognition and Pose Tracking [J]. | IEEE Transactions on Cybernetics , 2016 , 46 (12) : 3032-3046 .
MLA Jiang, Ping et al. "Unfalsified Visual Servoing for Simultaneous Object Recognition and Pose Tracking" . | IEEE Transactions on Cybernetics 46 . 12 (2016) : 3032-3046 .
APA Jiang, Ping , Cheng, Yongqiang , Wang, Xiaonian , Feng, Zuren . Unfalsified Visual Servoing for Simultaneous Object Recognition and Pose Tracking . | IEEE Transactions on Cybernetics , 2016 , 46 (12) , 3032-3046 .
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Using a wireless visual sensor network to harmonically navigate multiple low-cost wheelchairs in an indoor environment EI SCIE Scopus
期刊论文 | 2016 , 62 , 88-99 | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS | IF: 3.5
WoS CC Cited Count: 2
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Abstract :

Harmonic navigation of multiple low-cost robotic wheelchairs in a topology of wireless sensor nodes that are deployed in a dynamic and crowded indoor environment is a Non-deterministic Polynomial-time hard (NP-hard) problem. To address this problem, we propose a distributed multi-wheelchair global harmonic navigation algorithm. The distinguishing features of the proposed navigation algorithm are global search and local conflict resolution abilities. In the proposed algorithm, a travel time prediction method adopts a penalty for potential conflicts based on wheelchairs' priority, velocity and distance between the nodes. Moreover, three harmonic rules are proposed for: (1) giving the highest priority to humans, (2) giving the highest priority to wheelchairs, (3) giving flexible priority to wheelchairs. Through extensive quantitative simulations, we explore the performance of wheelchairs in various floor plan topologies and different values for the system parameters, and demonstrate that the properties of crowded indoor environments have important influence on the performance of global navigation, such as service time. The third harmonic rule establishes the trade-off between the performance of humans and robotic wheelchairs. At the same time, physical prototype wheelchairs are implemented and they verify the proposed global harmonic navigation algorithm. Some suggestions for robotic wheelchair designers, building architects and building owners are provided based on the conclusion of the experimental results. (C) 2015 Elsevier Ltd. All rights reserved.

Keyword :

Indoor environment Harmonic navigation Low-cost wheelchair Multiple wheelchair navigation Wireless visual sensor network

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GB/T 7714 Tian, Feng , Chao, Kuo-Ming , Feng, Zuren et al. Using a wireless visual sensor network to harmonically navigate multiple low-cost wheelchairs in an indoor environment [J]. | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2016 , 62 : 88-99 .
MLA Tian, Feng et al. "Using a wireless visual sensor network to harmonically navigate multiple low-cost wheelchairs in an indoor environment" . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 62 (2016) : 88-99 .
APA Tian, Feng , Chao, Kuo-Ming , Feng, Zuren , Xing, Keyi , Shah, Nazaraf . Using a wireless visual sensor network to harmonically navigate multiple low-cost wheelchairs in an indoor environment . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2016 , 62 , 88-99 .
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