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A Fast and Accurate Face Tracking Scheme by using Depth Information in Addition to Texture Information

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2014年第2期9卷 707-720页

作者： Kim, Dong-Wook Kim, Woo-Youl Yoo, Jisang Seo, Young-Ho Kwangwoon Univ Dept Elect Mat Engn Seoul South Korea Kwangwoon Univ Coll Liberal Arts Seoul South Korea

This paper proposes a face tracking scheme that is a combination of a face detection algorithm and a face tracking algorithm. The proposed face detection algorithm basically uses the adaboost algorithm, but the amount of search area is dramatically reduced, by using skin color and motion information in the depth map. Also, we propose a face tracking algorithm that uses a template matching method with depth information only. It also includes an early termination scheme, by a spiral search for template matching, which reduces the operation time with small loss in accuracy. It also incorporates an additional simple refinement process to make the loss in accuracy smaller. When the face tracking scheme fails to track the face, it automatically goes back to the face detection scheme, to find a new face to track. The two schemes are experimented with some home-made test sequences, and some in public. The experimental results are compared to show that they outperform the existing methods in accuracy and speed. Also we show some trade-offs between the tracking accuracy and the execution time for broader application.

关键词： Face detection Face tracking adaboost algorithm Template resizing Early termination

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Front vehicle detection based on multi-sensor fusion for autonomous vehicle

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第1期38卷 365-377页

作者： Zhang, Shuo Zhao, Xuan Lei, Wubin Yu, Qiang Wang, Yibo Changan Univ Sch Automobile Xian Peoples R China SAIC MOTOR Tech Ctr Shanghai Peoples R China

On account of the limitations of single sensor in obstacle detection, the paper investigates an obstacle detection method based on the fusion of 3D LiDAR and monocular visual. The spatial data fusion of the two sensors is realized according to their calibration results, and the time data fusion is realized by using double buffer technology. Considering the aspect ratio of vehicles, the image region of interest is determined based on the obstacle clustering of 3D LiDAR data. By using Haar-like features as effective characteristic of the front vehicle, integral figure is applied to extract Haar-like features of vehicle samples and non-vehicle samples. adaboost algorithm is used to choose weak classifiers to constitute strong classifiers, which combine into the cascade classifier. The cascade classifier has been trained to identify the vehicle target in the image region of interest. The relevant experimental results verify the effectiveness and real-time performance of the detection method.

关键词： Obstacle detection multi-sensor fusion vehicle identification adaboost algorithm

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A smoke detection system using colour features and the Ada Boost algorithm

A smoke detection system using colour features and the Ada B...

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2014 International Conference on Artificial Intelligence and Industrial Application (AIIA2014)

作者： E.H.Choi J.G.Song B.W.Yun J.S.Park H.T.Kim Department Multimedia Engineering Dongeui University Department Electronics Engineering Kyungsung University

ISBN: (纸本)9781784660529

In this paper, an early smoke detection system was proposed using the colour features and dynamic characteristics of smoke. The first step is to extract the background images from a CCTV input video signal using the Gaussian mixture model. Then the foreground images are obtained through subtracting the operation between the input image and the background image. The foreground images, separated through a mathematical morphology operation, are considered as the candidate area. By analysing the colour information and dynamic characteristics of the candidate area, smoke is finally determined. Through the experiments with input videos from a CCTV camera, the proposed algorithms were useful for detecting smoke.

关键词： smoke detection colour feature morphological feature adaboost algorithm

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Dialect/accent classification using unrestricted audio

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2007年第2期15卷 453-464页

作者： Huang, Rongqing Hansen, John H. L. Angkititrakul, Pongtep Univ Texas Dept Elect Engn Ctr Robust Speech Syst Richardson TX 75083 USA

This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based system with significantly less computational costs. The new algorithm, which is named Word-based Dialect Classification (WDC), converts the text-independent decision problem into a text-dependent decision problem and produces multiple combination decisions at the word level rather than making a single decision at the utterance level. The basic WDC algorithm also provides options for further modeling and decision strategy improvement. Two sets of classifiers are employed for WDC: a word classifier D-W(k) and an utterance classifier D-u. D-W(k) is boosted via the adaboost algorithm directly in the probability space instead of the traditional feature space. D. is boosted via the dialect dependency information of the words. For a small training corpus, it is difficult to obtain a robust statistical model for each word and each dialect. Therefore, a context adapted training (CAT) algorithm is formulated, which adapts the universal phoneme Gaussian mixture models (GMMs) to dialect-dependent word hidden Markov models (HMMs) via linear regression. Three separate dialect corpora are used in the evaluations that include the Wall Street Journal (American and British English), NATO N4 (British, Canadian, Dutch, and German accent English), and IME (eight British dialects). Significant improvement in dialect classification is achieved for all corpora tested.

关键词： accent/dialect classification adaboost algorithm context adapted trianing dialect dependency information limited training data robust acoustic modeling word-based modeling

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Dependence structure of the commodity and stock markets, and relevant multi-spread strategy

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2011年第21-22期390卷 3842-3854页

作者： Kim, Min Jae Kim, Sehyun Jo, Yong Hwan Kim, Soo Yong Korea Adv Inst Sci & Technol Dept Phys Taejon South Korea

Understanding the dependence structure between the commodity and stock markets is a crucial issue in constructing a portfolio. It can also help us to discover new opportunities to implement spread trading using multiple assets classified in the two different markets. This study analyzed the dependence structure of the commodity and stock markets using the random matrix theory technique and network analysis. Our results show that the stock and commodity markets must be handled as completely separated asset classes except for the oil and gold markets, so the performance enhancement of the mean-variance portfolio is significant as expected. In light of the fact that WTI 1 month futures and four oil-related stocks are strongly correlated, they were selected as basic ingredients to complement the multi-spread convergence trading strategy using a machine learning technique called the adaboost algorithm. The performance of this strategy for non-myopic investors, who can endure short-term loss, can be enhanced significantly on a risk measurement basis. Crown Copyright (C) 2011 Published by Elsevier B.V. All rights reserved.

关键词： Commodity Random matrix theory Network analysis Multi-spread trading adaboost algorithm

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Prediction of hot metal temperature based on data mining

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HIGH TEMPERATURE MATERIALS AND PROCESSES 2021年第1期40卷 87-98页

作者： Jun, Zhao Xin, Li Song, Liu Kun, Wang Qing, Lyu Erhao, Liu North China Univ Sci & Technol Coll Met & Energy Tangshan 063009 Peoples R China Northeastern Univ Coll Met Shenyang 110004 Peoples R China Tangshan Coll Dept Comp Sci & Technol Tangshan 063000 Peoples R China Chengde Iron & Steel Grp Co Ltd Tech Ctr Chengde 067000 Peoples R China

Accurately and continuously monitoring the hot metal temperature status of the blast furnace (BF) is a challenging job. To solve this problem, we propose a hot metal temperature prediction model based on the adaboost integrated algorithm using the real production data of the BF. We cleaned the raw data using the data analysis technology combined with metallurgical process theory, which mainly included data integration, outliers elimination, and missing value supplement. The redundant features were removed based on Pearson's thermodynamic diagram analysis, and the input parameters of themodel were preliminarily determined by using recursive feature elimination method. We built the hot metal temperature prediction model using the adaboost ensemble algorithm on a dataset with selected features as well as derived features by using K-mean clustering tags. The results show that the performance of the hot metal temperature prediction model with K-means clustering tags has been further improved, and the accurate monitoring and forecast of molten iron temperature has been achieved. The model can achieve an accuracy of more than 90% with an error of +/- 5 degrees C.

关键词： hot metal temperature adaboost algorithm K-means clustering correlation coefficient

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Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor

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SENSORS 2022年第16期22卷 520-537页

作者： Xue, Hongtao Song, Ziwei Wu, Meng Sun, Ning Wang, Huaqing Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Jiangsu Peoples R China Bosch Automot Prod Suzhou Co Ltd Suzhou 215021 Peoples R China Nanjing Forestry Univ Coll Automot & Traff Engn Nanjing 210037 Peoples R China Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing 100029 Peoples R China

To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a K-means clustering and adaboost algorithm to solve the lower accuracy and poorer stability of traditional AHNs. Firstly, K-means clustering is used to improve the interval updating method of any adjacent AHNs molecules, and then simplify the complexity of the AHNs model. Secondly, the adaboost algorithm is utilized to adaptively distribute the weights for multiple weak models, then reconstitute the network structure of the AHNs. Finally, double-optimized AHNs are used to build an intelligent diagnosis system, where two cases of bearing datasets from Paderborn University and a self-made IWM test stand are processed to validate the better performance of the proposed method, especially in multiple rotating speeds and the load conditions of the IWM. The double-optimized AHNs provide a higher accuracy for identifying the mechanical faults of the IWM than the traditional AHNs, K-means-based AHNs (K-AHNs), support vector machine (SVM), and particle swarm optimization-based SVM (PSO-SVM).

关键词： intelligent diagnosis in-wheel motor artificial hydrocarbon networks K-means clustering adaboost algorithm

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MutualBoost learning for selecting Gabor features for face recognition

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PATTERN RECOGNITION LETTERS 2006年第15期27卷 1758-1767页

作者： Shen, Linlin Bai, Li Univ Nottingham Sch Comp Sci & IT Nottingham NG8 1BB England

This paper describes an improved boosting algorithm, the MutualBoost algorithm, and its application in developing a fast and robust Gabor feature based face recognition system. The algorithm uses mutual information to eliminate redundancy among Gabor features selected using the adaboost algorithm. Selected Gabor features are then subjected to Generalized Discriminant Analysis (GDA) for class separability enhancement before being used for face recognition. Compared with one of the top performers in the 2004 face verification competition, our method demonstrates clear advantages in classification accuracy, memory and computation. The method has been tested on the whole FERET database using the FERET evaluation protocol. Significant improvement in performance is observed. For example, existing Gabor based methods use a huge number of Gabor features, our method needs only hundreds of Gabor features to achieve very high classification accuracy. Due to substantially reduced feature dimension, memory and computation costs are reduced significantly - only 4 s are needed to recognize 200 face images. (c) 2006 Elsevier B.V. All rights reserved.

关键词： Gabor filters adaboost algorithm generalized discriminant analysis

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A robust temperature prediction model of shuttle kiln based on ensemble random vector functional link network

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APPLIED THERMAL ENGINEERING 2019年 150卷 99-110页

作者： Zhang, Lei Zhang, Xiaogang Chen, Hua Tang, Hongzhong Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Xiangtan Univ Coll Informat Engn Xiangtan 411105 Hunan Peoples R China

In the sintering process for shuttle kiln, the sintering temperature is the most important thermal parameter, which plays a critical role in stable and efficient operation of the production process. However, due to the complex structure of shuttle kiln, this poses a great challenge for accurate and reliable online prediction of sintering temperature. Therefore, in this paper, a novel prediction model based on ensemble random vector functional link network (AB-RRVFLN) is proposed to predict the temperature. First, the mutual information method is proposed to select the optimal variables for prediction model. Then, considering the outlier influence of industrial data, a robust random vector functional link network (RRVFLN) based on the iteratively reweighted least squares is developed to build prediction model. Moreover, adaboost strategy is integrated with RRVFLN model to enhance prediction performance. Finally, the model output is further corrected by offset compensation technique. Based on industrial data from shuttle kiln, the application results on sintering temperature prediction demonstrate that the proposed ensemble method achieves better accuracy and shows stronger robustness than other methods.

关键词： Shuttle kiln Sintering temperature prediction Random vector functional link network (RVFLN) adaboost algorithm Offset compensation

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Key Technologies of Intelligent Transportation Based on Image Recognition and Optimization Control

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第10期34卷

作者： Huang, Mingxia Yan, Xuebo Bai, Zhu Zhang, Haiqiang Xu, Zeen Shenyang Jianzhu Univ Sch Transportat Engn 9 Hunnandonglu Shenyang 110168 Liaoning Peoples R China Shenzhen Juxin Image Co Ltd Shenzhen Peoples R China Wonkwang Univ Grad Sch Dept Informat Management Iksan South Korea

With the development of digital image processing technology, the application scope of image recognition is more and more wide, involving all aspects of life. In particular, the rapid development of urbanization and the popularization and application of automobiles in recent years have led to a sharp increase in traffic problems in various countries, resulting in intelligent transportation technology based on image processing optimization control becoming an important research field of intelligent systems. Aiming at the application demand analysis of intelligent transportation system, this paper designs a set of high-definition bayonet systems for intelligent transportation. It combines data mining technology and distributed parallel Hadoop technology to design the architecture and analysis of intelligent traffic operation state data analysis. The mining algorithm suitable for the system proves the feasibility of the intelligent traffic operation state data analysis system with the actual traffic big data experiment, and aims to provide decision-making opinions for the traffic state. Using the deployed Hadoop server cluster and the adaboost algorithm of the improved MapReduce programming model, the example runs large traffic data, performs traffic analysis and speed-overspeed analysis, and extracts information conducive to traffic control. It proves the feasibility and effectiveness of using Hadoop platform to mine massive traffic information.

关键词： Image recognition intelligent traffic Hadoop adaboost algorithm

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