Proposed Feature Extraction Scheme Research Articles

Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification.

Objective.Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using resting-state functional magnetic resonance imaging data.Approach.A dynamic sliding-windowed non-negative block-diagonal representation (dNBDR) method is proposed for constructing dynamic BFN, based on which a collection of dynamic BFN measures are suggested for examining age-related differences at the group level and used as features for brain age classification at the individual level.Results.The experimental results reveal that the dNBDR method is superior to the sliding time window with Pearson correlation method in terms of dynamic network structure quality. Additionally, significant alterations in dynamic BFN structures exist across the human lifespan. Specifically, average node flexibility and integration coefficient increase with age, while the recruitment coefficient shows a decreased trend. The proposed feature extraction scheme based on dynamic BFN achieved the highest accuracy of 78.7% in classifying three brain age groups.Significance. These findings suggest that dynamic BFN measures, dynamic community structure metrics in particular, play an important role in quantitatively assessing brain aging.

Integrated framework for EMD–Boruta-LDA feature extraction and SVM classification in coal and gas outbursts

ABSTRACT Coal and gas outbursts classification has become more important than before due to the serious threat to the safety of coal production, in this paper, we proposed a novel combination model consists of feature decomposition and reconstruction, feature selection and feature extraction for classification of coal and gas outbursts. First, EMD is used to decompose the coal and gas outbursts index features into a number of different IMFS; Second, in order to find out the relevance of IMFS with regard to the features, a wrapper algorithm Boruta with the RF classifier is employed, and the IMFS which has high relevance with the feature are selected to form a new index feature, then the new obtained features construct new influencing factors that affect coal and gas outbursts; Furtherly, in order to eliminate the redundancy between the new generated features and the uncorrelation between the index features and outbursts, the LDA is used to extract the features with class differentiation. Finally, the SVM classifiers based on the optimal parameters by Bayesian optimisation algorithm is employed to evaluate the proposed feature extraction scheme. Experimental results show that the proposed comprehensive model can achieve significant performance in terms of classification accuracy and feature size compared to existing methods for coal and gas outbursts classification.

Detection of Obstructive Sleep Apnea Using Non-Negative Matrix Factorization-Based Feature Extraction Approach in Eigen Spectrum Domain

Obstructive sleep apnea (OSA) is the most prevalent type sleeping disorder that highly affects people’s health impairment by threatening the cardiovascular system. As an alternative to standard polysomnography (PSG) analysis, the electrocardiogram (ECG) signal has gained high interests in recent studies of OSA diagnosis. However, the existing techniques are unable to provide high detection accuracy due to their inability of capturing the hidden patterns underlying in the EGC signal. In this context, the proposed OSA detection model is designed in terms of the singular spectrum analysis (SSA) of the ECG signal. This approach investigates the Eigen spectrum of ECG data obtained from SSA. In addition, a non-negative matrix factorization (NMF) scheme is applied to reduce the dimensionality of Eigen space by eliminating its redundant characteristics. Following this, a set of new feature predictors is extracted from the activation matrices. Moreover, the present work employs an ensemble learning method by fusing diverse and accurate base classifiers to realize a high-performance diagnostic system. This study has achieved overall 94.35% accuracy for minute-by-minute OSA detection using Physionet Apnea-ECG and University College Dublin (UCD) Database. Comparative study with the existing methods shows satisfactory performances of the proposed feature extraction scheme and ensemble learning approach.

Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise.

Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy (PE) has the advantages of high efficiency and simple calculation. However, PE has the problems of missing amplitude information and single scale. To address the two drawbacks, refined composite multi-scale reverse weighted PE (RCMRWPE), as a novel measurement technology of describing the signal complexity, is put forward based on refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE). RCMP is an improved method of coarse-graining, which not only solves the problem of single scale, but also improves the stability of traditional coarse-graining; RWPE has been proposed more recently, and has better inter-class separability and robustness performance to noise than PE, weighted PE (WPE), and reverse PE (RPE). Additionally, a feature extraction scheme of ship-radiated noise is proposed based on RCMRWPE, furthermore, RCMRWPE is combined with discriminant analysis classifier (DAC) to form a new classification method. After that, a large number of comparative experiments of feature extraction schemes and classification methods with two artificial random signals and six ship-radiated noise are carried out, which show that the proposed feature extraction scheme has better performance in distinguishing ability and stability than the other three similar feature extraction schemes based on multi-scale PE (MPE), multi-scale WPE (MWPE), and multi-scale RPE (MRPE), and the proposed classification method also has the highest recognition rate.

Subspace-based feature extraction on multi-physiological measurements of automobile drivers for distress recognition

The automotive industry has accelerated the utilization of Intelligent Transport Systems (ITS) in vehicles for increased driving safety. In this paper, a novel and well-done subspace feature extraction scheme on the physiological signals acquired by wearable sensors, for drivers’ distress level detection to be introduced as an ITS is proposed and verified on the publicly available MIT-BIH PhysioNet Multi-parameter Database. The proposed scheme includes two phases where time-domain statistical feature extraction is first realized on the electrocardiogram (ECG), hand galvanic skin response (hand GSR), foot galvanic skin response (foot GSR), electromyogram (EMG), and respiration (RESP) signals, and secondly subspace feature vector construction is appreciated by applying Discriminative Common Vector (DCV) decomposition on the statistical feature vectors. The distress levels of the drivers are determined as low, moderate, and high by utilizing both the statistical and the subspace feature vectors using Support Vector Machines (SVM) classifier by 2-fold cross-validation technique. A maximum of 88.89 % classification accuracy is achieved using statistical features in 7384 s while it is increased to 100 % in 3,421 s when subspace features are employed. The increased classification accuracy in decreased time consumption evidently shows the success of the proposed feature extraction scheme.

Discrimination of Life-Threatening Arrhythmias Using Singular Value, Harmonic Phase Distribution, and Dynamic Time Warping of ECG Signals

The automated analysis of electrocardiogram (ECG) signal is crucial for early recognition of life-threatening arrhythmias. This work presents an efficient ECG feature description model for diagnosis of ventricular arrhythmias (VAs); ventricular fibrillation (VF) and ventricular tachycardia (VT). The concept of a new feature set by applying singular value decomposition (SVD) and harmonic phase distribution is introduced to capture the morphological variation of the ECG signal. The phase characterization and singular value features are extracted from discrete Fourier transform and Empirical mode decomposition of ECG signal, respectively. In addition to that, dynamic time warping (DTW) procedure is applied to measure the similarity/dissimilarity pattern of ECG signals for classifying VAs. The firstfold classification involves the discrimination of VAs and non-VA patterns. Following this, secondfold classification discriminates VF versus VT and normal rhythms from other arrhythmic conditions. Experimental results are validated using the benchmark Physionet ECG database. The proposed method achieved the best performances using 5-s ECG segment with tenfold cross validation strategy. A comparative study with existing methods also shows superior performances of the proposed feature extraction scheme.

Adaptive Slide Window-Based Feature Cognition for Deceptive Information Identification

Numerous false information come up to be the primary threat for regular communication and cooperation, which always have similar expressions and diffusion patterns with regular information. To recognize deceptive information and alleviate their threat against business and economy, a number of research approaches for deceptive information identification have been proposed. However, these approaches suffer from their rough feature extraction processes, and thus cannot distinguish deceptive information from others. We introduce an adaptive slide-window based feature extraction, which captures semantic features and eliminates the trivial parts from them with adaptive slide windows on sentence elements, in order to facilitate accurate semantic structure representation of various texts. In addition, we propose a deep deceptive information identification model based on the proposed feature extraction scheme. Experiments on three real-world datasets demonstrate that the proposed deep deceptive information identification model can distinguish deceptive information from regular information accurately by extracting the significant features instead of trivial common expressions.

Language-based document image retrieval for Trilingual System

Language-based document image retrieval (LBDIR) is an essential need for a multi-lingual environment. It provides an ease of accessing, searching and browsing of the documents pertaining to a particular language. This paper proposes a method for LBDIR using multi-resolution Histogram of Oriented Gradient (HOG) features. These features are obtained by computing HOG on the sub-bands of Discrete Wavelet Transform. The Canberra distance is used for matching and retrieval of the documents. The proposed scheme is investigated on the three datasets (Dataset1, Dataset2 and Dataset3) consisting of 1437 document images of Kannada, Marathi, Telugu, Hindi and English languages. The objective of this work is to provide an efficient LBDIR for the government and non-government organizations of Karnataka, Maharashtra and Telangana states with the context of the tri-lingual model adopted. An average precision (AP) of 96.2%, 95.4%, 94.6%, 99.4% and 99.6% for Kannada, Marathi, Telugu, Hindi and English language documents is achieved while retrieving top 50 documents with the proposed method. The proposed feature extraction scheme provided promising results compared to existing techniques.

Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting

Background and objective:Automatic sleep staging is essential for alleviating the burden of the physicians of analyzing a large volume of data by visual inspection. It is also a precondition for making an automated sleep monitoring system feasible. Further, computerized sleep scoring will expedite large-scale data analysis in sleep research. Nevertheless, most of the existing works on sleep staging are either multichannel or multiple physiological signal based which are uncomfortable for the user and hinder the feasibility of an in-home sleep monitoring device. So, a successful and reliable computer-assisted sleep staging scheme is yet to emerge.Methods:In this work, we propose a single channel EEG based algorithm for computerized sleep scoring. In the proposed algorithm, we decompose EEG signal segments using Ensemble Empirical Mode Decomposition (EEMD) and extract various statistical moment based features. The effectiveness of EEMD and statistical features are investigated. Statistical analysis is performed for feature selection. A newly proposed classification technique, namely – Random under sampling boosting (RUSBoost) is introduced for sleep stage classification. This is the first implementation of EEMD in conjunction with RUSBoost to the best of the authors’ knowledge. The proposed feature extraction scheme’s performance is investigated for various choices of classification models. The algorithmic performance of our scheme is evaluated against contemporary works in the literature.Results:The performance of the proposed method is comparable or better than that of the state-of-the-art ones. The proposed algorithm gives 88.07%, 83.49%, 92.66%, 94.23%, and 98.15% for 6-state to 2-state classification of sleep stages on Sleep-EDF database. Our experimental outcomes reveal that RUSBoost outperforms other classification models for the feature extraction framework presented in this work. Besides, the algorithm proposed in this work demonstrates high detection accuracy for the sleep states S1 and REM.Conclusion:Statistical moment based features in the EEMD domain distinguish the sleep states successfully and efficaciously. The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research.

Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

Abstract Automatic sleep apnea detection using single lead ECG is a precondition for the implementation of a sleep apnea monitoring device. Computerized sleep apnea screening is also essential for expediting sleep apnea research and alleviating the onus of physicians of analyzing a large volume of data by visual inspection. However, most of the state-of-the-art works on automated sleep apnea identification are either based on multiple leads and multiple physiological signals or yield poor performance. In this article, normal inverse Gaussian (NIG) pdf modeling in the recently proposed tunable-Q factor wavelet transform (TQWT) domain is introduced for computer-assisted sleep apnea diagnosis from single-lead ECG signals. First, ECG signal segments are decomposed into sub-bands using TQWT. Afterwards, the corresponding NIG parameters are computed from each of the sub-bands. These parameters are used as features in the proposed apnea detection algorithm. Adaptive boosting (AdaBoost), an eminent ensemble learning based classification scheme is employed to perform classification. The suitability of TQWT is analyzed. The effectiveness of the selected features is validated by intuitive, statistical, and graphical analyses. The performance of the proposed feature extraction scheme is evaluated for various choices of classifiers. Optimal choices of TQWT and AdaBoost parameters are also determined. The performance of the proposed method, as compared to the state-of-the-art algorithms, is comparable or superior in terms of various performance metrics.

Classification of motor imagery movements using multivariate empirical mode decomposition and short time Fourier transform based hybrid method

Effective online processing of electroencephalogram (EEG) signals is a prerequisite of brain computer interfacing (BCI). In this paper, we propose a hybrid method consisting of multivariate empirical mode decomposition (MEMD) and short time Fourier transform (STFT) to identify left and right hand imaginary movements from EEG signals. Experiments are carried out using the publicly available benchmark BCI competition II Graz motor imagery data base. The EEG epochs are decomposed into multiple intrinsic mode functions (IMFs) by applying MEMD. The most significant mode is subjected to the short time Fourier transform; the peak of the magnitude spectrum is used as feature representing the corresponding epoch. The efficacy of the proposed feature extraction scheme is demonstrated by intuitive, statistical and graphical analyses. The performance of the proposed feature extraction scheme is investigated for various choices of classifiers. Our findings suggest that k-Nearest Neighbor (kNN) emerges as the best classification model yielding 90.71% accuracy. The performance of our method is also compared to that of existing works in the literature. Experimental outcomes backed by statistical validation manifest that the performance of the proposed method is comparable or better than many of the state-of-the-art algorithms.

Computer-aided obstructive sleep apnea identification using statistical features in the EMD domain and extreme learning machine

Automated sleep apnea identification is important in order to eradicate the clinicians duty of analyzing a large volume of data and to speed-up diagnosis. Again, the feasibility of a wearable and portable sleep quality evaluation device necessitates the use of a minimum number of leads. As a result, there is a dire need for a single-lead based apnea detection algorithm. In this work, a single-lead electrocardiogram (ECG) based computerized sleep apnea detection scheme is propounded. ECG signal segments are first decomposed using Empirical Mode Decomposition. Various statistical moment based features are then extracted. After performing statistical analysis for feature selection, sleep apnea classification is performed using a newly proposed classification model, namely- extreme learning machine. The efficacy and the discrimination capability of statistical features are evaluated. The performance of the proposed feature extraction scheme is also analyzed for various classification models. Experimental outcomes backed by statistical and graphical analyses suggest that the proposed sleep apnea detection scheme outperforms the state-of-the-art ones in terms of accuracy.

Classification of short-circuit faults in high-voltage energy transmission line using energy of instantaneous active power components-based common vector approach

The majority of power system faults occur in transmission lines. The classication of these faults in power systems is an important issue. In this paper, the real parameters of a 28 km, 154 kV transmission line between Simav and Demirci in Turkey's electricity transmission network is simulated in MATLAB/Simulink. Wavelet packet transform (WPT) is applied to instantaneous voltage signals. Instantaneous active power components are obtained by multiplying instantaneous currents obtained from a voltage source side with these WPT-based voltage signal components. A new feature vector extraction scheme is employed by calculating the energies of instantaneous active power components. Constructed feature vectors are treated with a classier for short-circuit faults that occurred in high-voltage energy transmission lines; this is known as the common vector approach (CV A). This is therst implementation of CV A in the classication of short-circuit faults that occurred in high-voltage energy transmission lines. Furthermore, the same feature vector is applied to a support vector machine and articial neural network for a comparison with the CV A method regarding classication performance and testing duration issues. Additionally, a graphical user interface is designed in MATLAB/GUI. Various noise levels, source frequencies, fault distances, fault inception angles, and fault exposure durations can be investigated with this interface. Classication of short-circuit faults in high-voltage transmission line is achieved by using an offline monitoring methodology. It is concluded that a combination of the proposed feature extraction scheme with the CV A classier gives substantially high performance for the classication of short circuit faults in transmission line.

Robust Texture Image Representation by Scale Selective Local Binary Patterns.

Local binary pattern (LBP) has successfully been used in computer vision and pattern recognition applications, such as texture recognition. It could effectively address grayscale and rotation variation. However, it failed to get desirable performance for texture classification with scale transformation. In this paper, a new method based on dominant LBP in scale space is proposed to address scale variation for texture classification. First, a scale space of a texture image is derived by a Gaussian filter. Then, a histogram of pre-learned dominant LBPs is built for each image in the scale space. Finally, for each pattern, the maximal frequency among different scales is considered as the scale invariant feature. Extensive experiments on five public texture databases (University of Illinois at Urbana-Champaign, Columbia Utrecht Database, Kungliga Tekniska Högskolan-Textures under varying Illumination, Pose and Scale, University of Maryland, and Amsterdam Library of Textures) validate the efficiency of the proposed feature extraction scheme. Coupled with the nearest subspace classifier, the proposed method could yield competitive results, which are 99.36%, 99.51%, 99.39%, 99.46%, and 99.71% for UIUC, CUReT, KTH-TIPS, UMD, and ALOT, respectively. Meanwhile, the proposed method inherits simple and efficient merits of LBP, for example, it could extract scale-robust feature for a 200×200 image within 0.24 s, which is applicable for many real-time applications.

Spectro-Temporal Gabor Filterbank Features for Acoustic Event Detection

Algorithms for the automatic detection and recognition of acoustic events are increasingly gaining relevance for the reliable and robust functioning of consumer, assistive and monitoring systems. The extraction of appropriate task relevant acoustic features from the raw sound signal clearly influences performance of subsequent statistical classification, in particular in adverse acoustic situations. The present contribution investigates the use of biologically-inspired features, derived from a filter-bank of two-dimensional Gabor functions, that decompose the spectro-temporal power density into components which capture spectral, temporal and joint spectro-temporal modulation patterns. It is hypothesized that the comparably large joint spectral and temporal extent of these Gabor functions results in features that allow for robust classification. Evaluation of the proposed feature extraction scheme together with an hidden Markov model (HMM) classifier is conducted on two corpora comprising acoustic events in realistic adverse conditions from the D-CASE and CLEAR'07 evaluation campaigns. Relevance of each Gabor filter for classification is analyzed and an optimized parameter set for the Gabor filterbank (GFB) is identified. Performance of the optimized GFB is evaluated in comparison to other state-of-the-art algorithms on isolated event classification and on the full acoustic event detection (AED) including joint classification and temporal segmentation of events. Results show that Gabor features result in a signal representation that exhibits separated average class-specific patterns. An improvement in classification accuracy of up to 26% relative to the Mel-frequency cepstral coefficient (MFCC) baseline is obtained with the optimized GFB. Further experiments demonstrate that this improvement cannot be explained by purely temporal or purely spectral Gabor basis functions. Rather, a GFB with features extending in joint spectro-temporal directions is required to obtain optimum performance. Performance on AED with the D-CASE challenge dataset is shown to improve on previous algorithms from the recent literature.

A sparse Bayesian learning based scheme for multi-movement recognition using sEMG.

This paper proposed a feature extraction scheme based on sparse representation considering the non-stationary property of surface electromyography (sEMG). Sparse Bayesian learning was introduced to extract the feature with optimal class separability to improve recognition accuracy of multi-movement patterns. The extracted feature, sparse representation coefficients (SRC), represented time-varying characteristics of sEMG effectively because of the compressibility (or weak sparsity) of the signal in some transformed domains. We investigated the effect of the proposed feature by comparing with other fourteen individual features in offline recognition. The results demonstrated the proposed feature revealed important dynamic information in the sEMG signals. The multi-feature sets formed by the SRC and other single feature yielded more superior performance on recognition accuracy, compared with the single features. The best average recognition accuracy of 94.33% was gained by using SVM classifier with the multi-feature set combining the feature SRC, Williston amplitude (WAMP), wavelength (WL) and the coefficients of the fourth order autoregressive model (ARC4) via multiple kernel learning framework. The proposed feature extraction scheme (known as SRC + WAMP + WL + ARC4) is a promising method for multi-movement recognition with high accuracy.

An Auditory Inspired Amplitude Modulation Filter Bank for Robust Feature Extraction in Automatic Speech Recognition

The human ability to classify acoustic sounds is still unmatched compared to recent methods in machine learning. Psychoacoustic and physiological studies indicate that the auditory system of mammals decomposes audio signals into their acoustic and modulation frequency components prior to further analysis. Since it is known that most linguistic information is coded in amplitude fluctuations, mimicking temporal processing strategies of the auditory system in automatic speech recognition (ASR) promises to increase recognition accuracies. We present an amplitude modulation filter bank (AMFB) that is used as a feature extraction scheme in ASR systems. The time-frequency resolution of the employed FIR filters, i.e., bandwidth and modulation frequency settings, are adopted from a psychophysically inspired model of Dau (1997) that was originally proposed to describe data from human psychoacoustics. Investigations on modulation phase indicate the need for preserving such information in amplitude modulation features. We show that the filter symmetry has an important impact on ASR performance. The proposed feature extraction scheme exhibits significant word error rate (WER) reductions using the Aurora-2, Aurora-4, and REVERB ASR tasks compared to other recent feature extraction methods, such as MFCC, FDLP, and PNCC features. Thereby, AMFB features reveal high robustness against additive noise, different transmission channel characteristics, and room reverberation. Using the Aurora-4 benchmark, for instance, an average WER of 12.33% with raw and 11.31% with bottleneck transformed features is attained, which constitutes a relative improvement of 19.6% and 29.2% over raw MFCC features, respectively.

A combined static and dynamic feature extraction technique to recognize handwritten digits

The recent advances in the feature extraction techniques in recognition of handwritten digits attract researchers to work in this area. The present study includes recognition of handwritten digits using hybrid feature extraction technique including static and dynamic properties of handwritten digit images. In this paper, static properties include number of non-zero (white) pixels in square, horizontal, vertical and diagonal styles as sub regions of a binary image. The dynamic properties include features from recovery of drawing order of original image. The extraction of dynamic features include two stages: first stage recover the drawing order of an image and second stage compute the chain code directions from recovered drawing order. The algorithm for recovery of drawing order uses properties of writing behavior. The support vector machine has been used as recognition method for the proposed feature extraction scheme. We have achieved an overall error rate of 0.73 % for mnist data set including 60,000 training images and 10,000 test images. Our feature extraction technique results in feature vector length of an image equals to 356. The achieved results strengthen our proposed technique usability as error rate achieved is at par with literature (\(<\)1 %) and the length of feature vector per image is small in comparison to input feature vector length of 784 which has been commonly used in previous work. The developed system is stable and useful in real life applications.

Smartphone based visible iris recognition using deep sparse filtering

Good biometric performance of iris recognition motivates it to be used for many large scale security and access control applications. Recent works have identified visible spectrum iris recognition as a viable option with considerable performance. Key advantages of visible spectrum iris recognition include the possibility of iris imaging in on-the-move and at-a-distance scenarios as compared to fixed range imaging in near-infra-red light. The unconstrained iris imaging captures the images with largely varying radius of iris and pupil. In this work, we propose a new segmentation scheme and adapt it to smartphone based visible iris images for approximating the radius of the iris to achieve robust segmentation. The proposed technique has shown the improved segmentation accuracy up to 85% with standard OSIRIS v4.1. This work also proposes a new feature extraction method based on deep sparse filtering to obtain robust features for unconstrained iris images. To evaluate the proposed segmentation scheme and feature extraction scheme, we employ a publicly available database and also compose a new iris image database. The newly composed iris image database (VSSIRIS) is acquired using two different smartphones – iPhone 5S and Nokia Lumia 1020 under mixed illumination with unconstrained conditions in visible spectrum. The biometric performance is benchmarked based on the equal error rate (EER) obtained from various state-of-art schemes and proposed feature extraction scheme. An impressive EER of 1.62% is obtained on our VSSIRIS database and an average gain of around 2% in EER is obtained on the public database as compared to the well-known state-of-art schemes.

Fall detection using single-tree complex wavelet transform

The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into “fall” and “ordinary activity” classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.