Indonesian Journal of Electrical Engineering and Computer Science
Modern video surveillance has now become an active area of research with a large set of requireme... more Modern video surveillance has now become an active area of research with a large set of requirements and various applications. In order to detect moving objects in video surveillance scenes, background subtraction techniques are the most used. In this paper, we developed and tested an efficient background subtraction technique in video surveillance based on the fast-independent component analysis (fast-ICA) method. The proposed technique initiated, first, on the use of a developed fast-ICA algorithm in order to estimate the de-mixing matrix and the denoising matrix parameters. Second, the estimated foreground can simply model by multiplying the data matrix with the de-mixing matrix. After that, the data matrix is multiplied by the denoising matrix for removing the noise. In addition, we propose a pre-processing and post-processing operations to effectively segment the true foreground objects and improve our results. The proposed method is evaluated on the publicly available change d...
2019 6th International Conference on Image and Signal Processing and their Applications (ISPA), 2019
In this paper, the long range target tracking problem is presented. Two versions of nonlinear Kal... more In this paper, the long range target tracking problem is presented. Two versions of nonlinear Kalman Filters are used to bring out their performances, the first one is the Extended Kalman Filter EKF and the second one is the Quadrature Kalman Filter (QKF). The problem addressed is the target tracking with high cross-range that moves in plan. The position measurements are provided by radar in polar coordinates. The simulations results show that, in terms of Root Mean Square Errors (RMSE), the QKF filter performs better than the classical EKF.
International journal of imaging and robotics, 2019
This paper presents a novel approach to solve the problem of estimating and tracking a moving tar... more This paper presents a novel approach to solve the problem of estimating and tracking a moving target, observed from projective viewpoint. A real-time tracking is achieved by a newly designed filter based on the steady state linear Kalman filter, known as αβ filter. The nonlinearity problem introduced by the projective nature of the observed data is handled using unscented transform, instead of the standard first order approximation. The effectiveness of the proposed filter is demonstrated and compared with that of existing methods by Monte-Carlo simulations.
Two new algorithms are presented in this paper to solve the bearing only tracking problem, by usi... more Two new algorithms are presented in this paper to solve the bearing only tracking problem, by using the recently proposed algorithms for nonlinear filtering, which are the Unscented Kalman Filter (UKF) and the Cubature Kalman Filter (CKF). These filters are used to solve the high intrinsic nonlinearity in the modified polar coordinates dynamic model. The resulting filters, named the Modified Polar Unscented Kalman Filter (MPUKF) and the Modified Polar Cubature Kalman Filter (MPCKF) are applied to the two dimensional bearing-only tracking problem. The estimation performance of these nonlinear filters is assessed by means of the root mean square error (RMSE). The simulation results show that these new filters are more efficient than other similar filters, such as the Modified Polar Extended Kalman Filter (MPEKF). Compared to this filter, the MPUKF and the MPCKF are, in particular, non-sensitive to the initial range and more stable, by using a dedicated initialization procedure, propos...
2016 4th International Conference on Control Engineering & Information Technology (CEIT), 2016
In this article, we present an implementation of the new digital communication, technology that u... more In this article, we present an implementation of the new digital communication, technology that uses visible light, known as LIFI (Light Fidelity) or VLC (Visual Light Communication), and apply it for inter-vehicle communication. This communication may improve driver's safety by allowing the vehicles to communicate easily with each other (V2V communication). The first prototype of an unidirectional VLC communication was developed at the laboratory of signals and images (LSI) of USTO-MB. The experimental results are more than satisfactory.
International Journal on Smart Sensing and Intelligent Systems, 2016
This paper addresses the problem of mobile sensor localization and tracking in an obstructed envi... more This paper addresses the problem of mobile sensor localization and tracking in an obstructed environment. To solve this problem, a combination of three approaches is proposed: a nonlinear Kalman Filter to estimate the mobile position, a sub filter used jointly with the nonlinear filter to estimate the bias due to the Non-Line Of Sight (NLOS) effect and a low complexity method for Line Of Sight (LOS) and NLOS identification. Based on hypothesis testing, this method discriminates between the LOS and NLOS situations using a sequence of estimated biases. Simulation results show that the proposed method provides good positioning accuracy
The 2nd International Conference on Control, Instrumentation and Automation, 2011
In a recent paper, a new discrete-time Bayesian filter, named the cubature Kalman filter (CKF), w... more In a recent paper, a new discrete-time Bayesian filter, named the cubature Kalman filter (CKF), was derived. To reduce the complexity of the filter, we propose in this paper to combine the CKF with the linear Kalman filter, when either the process equation or the measurement equation is linear. The resulting filter is referred to as the Reduced CKF (RCKF). It is here applied to the problem of tracking in Cartesian coordinates a moving object whose state can be modeled by a linear dynamic equation, but whose measurement equation is non linear, due to the fact that the measurements represent position measurements in polar coordinates. The simulations results show that, in terms of root Mean Square Error (RMSE), the RCKF and CKF have the same performance, but the processing time of the RCKF is lower than that of the CKF.
2013 11th International Symposium on Programming and Systems (ISPS), 2013
ABSTRACT In this paper we propose an algorithm for road traffic density estimation, using macrosc... more ABSTRACT In this paper we propose an algorithm for road traffic density estimation, using macroscopic parameters, extracted from a video sequence. Macroscopic parameters are directly estimated by analyzing the global motion in the video scene without the need of motion detection and tracking methods. The extracted parameters are applied to the SVM classifier, to classify the road traffic in three categories: light, medium and heavy. The performance of the proposed algorithm is compared to that of the texture dynamic based traffic road classification method, using the same data base.
Indonesian Journal of Electrical Engineering and Computer Science
Modern video surveillance has now become an active area of research with a large set of requireme... more Modern video surveillance has now become an active area of research with a large set of requirements and various applications. In order to detect moving objects in video surveillance scenes, background subtraction techniques are the most used. In this paper, we developed and tested an efficient background subtraction technique in video surveillance based on the fast-independent component analysis (fast-ICA) method. The proposed technique initiated, first, on the use of a developed fast-ICA algorithm in order to estimate the de-mixing matrix and the denoising matrix parameters. Second, the estimated foreground can simply model by multiplying the data matrix with the de-mixing matrix. After that, the data matrix is multiplied by the denoising matrix for removing the noise. In addition, we propose a pre-processing and post-processing operations to effectively segment the true foreground objects and improve our results. The proposed method is evaluated on the publicly available change d...
2019 6th International Conference on Image and Signal Processing and their Applications (ISPA), 2019
In this paper, the long range target tracking problem is presented. Two versions of nonlinear Kal... more In this paper, the long range target tracking problem is presented. Two versions of nonlinear Kalman Filters are used to bring out their performances, the first one is the Extended Kalman Filter EKF and the second one is the Quadrature Kalman Filter (QKF). The problem addressed is the target tracking with high cross-range that moves in plan. The position measurements are provided by radar in polar coordinates. The simulations results show that, in terms of Root Mean Square Errors (RMSE), the QKF filter performs better than the classical EKF.
International journal of imaging and robotics, 2019
This paper presents a novel approach to solve the problem of estimating and tracking a moving tar... more This paper presents a novel approach to solve the problem of estimating and tracking a moving target, observed from projective viewpoint. A real-time tracking is achieved by a newly designed filter based on the steady state linear Kalman filter, known as αβ filter. The nonlinearity problem introduced by the projective nature of the observed data is handled using unscented transform, instead of the standard first order approximation. The effectiveness of the proposed filter is demonstrated and compared with that of existing methods by Monte-Carlo simulations.
Two new algorithms are presented in this paper to solve the bearing only tracking problem, by usi... more Two new algorithms are presented in this paper to solve the bearing only tracking problem, by using the recently proposed algorithms for nonlinear filtering, which are the Unscented Kalman Filter (UKF) and the Cubature Kalman Filter (CKF). These filters are used to solve the high intrinsic nonlinearity in the modified polar coordinates dynamic model. The resulting filters, named the Modified Polar Unscented Kalman Filter (MPUKF) and the Modified Polar Cubature Kalman Filter (MPCKF) are applied to the two dimensional bearing-only tracking problem. The estimation performance of these nonlinear filters is assessed by means of the root mean square error (RMSE). The simulation results show that these new filters are more efficient than other similar filters, such as the Modified Polar Extended Kalman Filter (MPEKF). Compared to this filter, the MPUKF and the MPCKF are, in particular, non-sensitive to the initial range and more stable, by using a dedicated initialization procedure, propos...
2016 4th International Conference on Control Engineering & Information Technology (CEIT), 2016
In this article, we present an implementation of the new digital communication, technology that u... more In this article, we present an implementation of the new digital communication, technology that uses visible light, known as LIFI (Light Fidelity) or VLC (Visual Light Communication), and apply it for inter-vehicle communication. This communication may improve driver's safety by allowing the vehicles to communicate easily with each other (V2V communication). The first prototype of an unidirectional VLC communication was developed at the laboratory of signals and images (LSI) of USTO-MB. The experimental results are more than satisfactory.
International Journal on Smart Sensing and Intelligent Systems, 2016
This paper addresses the problem of mobile sensor localization and tracking in an obstructed envi... more This paper addresses the problem of mobile sensor localization and tracking in an obstructed environment. To solve this problem, a combination of three approaches is proposed: a nonlinear Kalman Filter to estimate the mobile position, a sub filter used jointly with the nonlinear filter to estimate the bias due to the Non-Line Of Sight (NLOS) effect and a low complexity method for Line Of Sight (LOS) and NLOS identification. Based on hypothesis testing, this method discriminates between the LOS and NLOS situations using a sequence of estimated biases. Simulation results show that the proposed method provides good positioning accuracy
The 2nd International Conference on Control, Instrumentation and Automation, 2011
In a recent paper, a new discrete-time Bayesian filter, named the cubature Kalman filter (CKF), w... more In a recent paper, a new discrete-time Bayesian filter, named the cubature Kalman filter (CKF), was derived. To reduce the complexity of the filter, we propose in this paper to combine the CKF with the linear Kalman filter, when either the process equation or the measurement equation is linear. The resulting filter is referred to as the Reduced CKF (RCKF). It is here applied to the problem of tracking in Cartesian coordinates a moving object whose state can be modeled by a linear dynamic equation, but whose measurement equation is non linear, due to the fact that the measurements represent position measurements in polar coordinates. The simulations results show that, in terms of root Mean Square Error (RMSE), the RCKF and CKF have the same performance, but the processing time of the RCKF is lower than that of the CKF.
2013 11th International Symposium on Programming and Systems (ISPS), 2013
ABSTRACT In this paper we propose an algorithm for road traffic density estimation, using macrosc... more ABSTRACT In this paper we propose an algorithm for road traffic density estimation, using macroscopic parameters, extracted from a video sequence. Macroscopic parameters are directly estimated by analyzing the global motion in the video scene without the need of motion detection and tracking methods. The extracted parameters are applied to the SVM classifier, to classify the road traffic in three categories: light, medium and heavy. The performance of the proposed algorithm is compared to that of the texture dynamic based traffic road classification method, using the same data base.
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