Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking

Li, Dawei; Xu, Lihong; Goodman, Erik D.; Xu, Yong; Wu, Yang

doi:10.3233/ica-130428

Cited by 58 publications

(30 citation statements)

References 44 publications

(50 reference statements)

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“…The highly ranked features are fed to a classification algorithm such as Support Vector Machine (SVM) [62,63,64], Decision Tree (DT) [65], K-Nearest Neighbour (KNN) [65]. The ten-fold cross validation method is often used to train and select the best classifier, which achieves high classification accuracy with a minimum number of features.…”

Section: Nonlinear Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis of Depression Using EEG Signals

Acharya

Sudarshan

Adeli³

et al. 2015

Eur Neurol

164

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The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very tedious to interpret visually and highly difficult to extract the significant features from them. The linear and nonlinear methods are effective in identifying the changes in EEG signals for the detection of depression. Linear methods do not exhibit the complex dynamical variations in the EEG signals. Hence, chaos theory and nonlinear dynamic methods are widely used in extracting the EEG signal features for computer-aided diagnosis (CAD) of depression. Hence, this article presents the recent efforts on CAD of depression using EEG signals with a focus on using nonlinear methods. Such a CAD system is simple to use and may be used by the clinicians as a tool to confirm their diagnosis. It should be of a particular value to enable the early detection of depression.

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Section: Nonlinear Methodsmentioning

confidence: 99%

Computer-Aided Diagnosis of Depression Using EEG Signals

Acharya

Sudarshan

Adeli³

et al. 2015

Eur Neurol

164

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“…The proposed technique worked efficiently with multiview and multi-posture problems. Dewei [30] introduced an online expectation-maximization (EM) algorithm in order to estimate foreground and background. Later, the human samples are cropped from the estimated foreground for HOG feature extraction.…”

Section: Related Workmentioning

confidence: 99%

A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection

Sharif

Khan

Akram

et al. 2017

J Image Video Proc.

107

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Human activity monitoring in the video sequences is an intriguing computer vision domain which incorporates colossal applications, e.g., surveillance systems, human-computer interaction, and traffic control systems. In this research, our primary focus is in proposing a hybrid strategy for efficient classification of human activities from a given video sequence. The proposed method integrates four major steps: (a) segment the moving objects by fusing novel uniform segmentation and expectation maximization, (b) extract a new set of fused features using local binary patterns with histogram oriented gradient and Harlick features, (c) feature selection by novel Euclidean distance and joint entropy-PCA-based method, and (d) feature classification using multi-class support vector machine. The three benchmark datasets (MIT, CAVIAR, and BMW-10) are used for training the classifier for human classification; and for testing, we utilized multi-camera pedestrian videos along with MSR Action dataset, INRIA, and CASIA dataset. Additionally, the results are also validated using dataset recorded by our research group. For action recognition, four publicly available datasets are selected such as Weizmann, KTH, UIUC, and Muhavi to achieve recognition rates of 95.80, 99.30, 99, and 99.40%, respectively, which confirm the authenticity of our proposed work. Promising results are achieved in terms of greater precision compared to existing techniques.

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“…If some players have similar technologies, it is possible to create stocks, as well as a single maintenance team for all the players; -Project -the experience acquired by the VPP will facilitate the new power plants licensing process. The advantages can be reflected at the bureaucratic level and to obtaining credit advantages; -Forecasting -to ensure a good operation, VPPs need a set of techniques to adequately forecast the consumption, the generation, the electric vehicles mobility [33] and the electricity market price [12]; -Energy Resources Management -the energy resources management will be conducted by the VPP, which will ease the players' operation; -Participation in demand response (DR) eventsVPPs should manage the participation of consumers and electric vehicles in DR events. VPPs can also develop specific DR programs dedicated to aggregated players.…”

Section: Virtual Power Playermentioning

confidence: 99%

Coalition of distributed generation units to Virtual Power Players - a game theory approach

Morais

Sousa

Santos

et al. 2015

ICA

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Abstract. Smart Grids (SGs) have emerged as the new paradigm for power system operation and management, being designed to include large amounts of distributed energy resources. This new paradigm requires new Energy Resource Management (ERM) methodologies considering different operation strategies and the existence of new management players such as several types of aggregators. This paper proposes a methodology to facilitate the coalition between distributed generation units originating Virtual Power Players (VPP) considering a game theory approach. The proposed approach consists in the analysis of the classifications that were attributed by each VPP to the distributed generation units, as well as in the analysis of the previous established contracts by each player. The proposed classification model is based in fourteen parameters including technical, economical and behavioural ones. Depending of the VPP strategies, size and goals, each parameter has different importance. VPP can also manage other type of energy resources, like storage units, electric vehicles, demand response programs or even parts of the MV and LV distribution network. A case study with twelve VPPs with different characteristics and one hundred and fifty real distributed generation units is included in the paper.

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Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking

Cited by 58 publications

References 44 publications

Computer-Aided Diagnosis of Depression Using EEG Signals

Computer-Aided Diagnosis of Depression Using EEG Signals

A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection

Coalition of distributed generation units to Virtual Power Players - a game theory approach

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