IMM fuzzy probabilistic data association algorithm for tracking maneuvering target

In this paper, a new interacting multiple model fuzzy probabilistic data association (IMM-FPDA) algorithm is proposed for tracking maneuvering target. In the proposed tracker, fuzzy logic is incorporated in a conventional IMM-PDA method. In order to determine process noise covariance of the Kalman filter used in IMM-PDA, the prediction error and change of the prediction error in the last prediction are used as fuzzy inputs. To optimize parameters of the fuzzy system, a tabu search algorithm is utilized. The IMM-FPDA tracker combines advantages of the FPDA and IMM algorithms. The performance of the proposed algorithm is compared with those of the IMM and PDA-IMM algorithms using two different maneuvering tracking scenarios. It is shown from simulation results that the IMM-FPDA algorithm greatly outperforms the IMM and IMM-PDA algorithms in terms of tracking error. (C) 2006 Elsevier Ltd. All rights reserved

Removing random-valued impulse noise in images using a neural network detector

This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images. The inputs of the neural model used to detect the RVIN are formed using basic and related gradient values. The detection of the noisy pixels is realized in 3 phases using the proposed neural detector. In order to obtain a more robust detector, 2 different networks, which are trained with an artificial training image corrupted with high and low clutter densities, are used. The extensive simulation results show that the proposed method is significantly better than the compared filters in terms of its image restoration and noise detection performance.: This paper proposes a new method using an arti?cial neural network to remove random-valued impulse noise(RVIN) in images. The inputs of the neural model used to detect the RVIN are formed using basic and related gradientvalues. The detection of the noisy pixels is realized in 3 phases using the proposed neural detector. In order to obtain amore robust detector, 2 di?erent networks, which are trained with an arti?cial training image corrupted with high andlow clutter densities, are used. The extensive simulation results show that the proposed method is signi?cantly betterthan the compared ?lters in terms of its image restoration and noise detection performance.</div

Efficient impulse noise detection method with ANFIS for accurate image restoration

This paper proposes a novel adaptive neuro-fuzzy inference system (ANFIS) based impulse detection method for the restoration of images corrupted by impulse noise (IN). After the corrupted pixels detected by proposed detector, the Median filtering is performed for only these pixels. The performance of the proposed neuro-fuzzy detector based median filter (NFDMF) is evaluated on different test images and compared with 14 different comparison filters from the literature. Experimental results show that the proposed filter shows better performance than the comparison filters in the cases of being effective in noise suppression and detail preservation, especially when the noise ratio is very high. (C) 2010 Elsevier GmbH. All rights reserved

A new method to remove random-valued impulse noise in images

This paper presents a new method for detecting random-valued impulse noise (RVIN) in images. The proposed method is based on similar valued neighbor criterion and the detection of the noisy pixels are realized in maximum four phases. After the corrupted pixels detected in each phase, the median filtering is performed for only these pixels. As such, corrupted pixels are suppressed gradually at the end of the each phase. The performance of the proposed method is evaluated on different test images and compared with ten different comparison filters from the literature. It is shown from simulation results that proposed method provides a significant improvement over comparison filters. (C) 2013 Elsevier GmbH. All rights reserved

Cheap joint probabilistic data association with adaptive neuro-fuzzy inference system state filter for tracking multiple targets in cluttered environment

The cheap joint probabilistic data association (CJPDA) with the adaptive neuro-fuzzy inference system state filter (ANFISSF) is presented for tracking multiple targets in the presence of low and high cluttered environments. The state update step of the CJPDA filter (CJPDAF) is realized with the ANFISSF instead of Kalman filter. The adaptive neuro-fuzzy inference system (ANFIS) has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The tracks estimated by using the method proposed in this paper for different tracking scenarios are in very good agreement with the original tracks

Tabu search tracker with adaptive neuro-fuzzy inference system for multiple target tracking

In this paper, a tabu search tracker with adaptive neurofuzzy inference system (TST-ANFIS) is presented for multiple target tracking (MTT). First, the data association problem, formulated as an N-dimensional assignment problem, is solved using the tabu search algorithm (TSA), and then the inaccuracies in the estimation are corrected by the adaptive neuro-fuzzy inference system (ANFIS). The performances of the TST-ANFIS, the joint probabilistic data association filter (JPDAF), the tabu search tracker (TST), Lagrangian relaxation algorithm (LRA), and cheap joint probabilistic data association with adaptive neuro-fuzzy inference system state filter (CJPDA-ANFISSF) are compared with each other for six different tracking scenarios. It was shown that the tracks estimated by using proposed TST-ANFIS agree better with the true tracks than the tracks predicted by the JPDAF, the TST, the LRA, and the CJPDA-ANFISSF

Cheap joint probabilistic data association with neural network state filter for tracking multiple targets in cluttered environment

In this paper a cheap joint probabilistic data association (CJPDA) with the neural network state filter (NNSF) is presented for tracking multiple targets in low and high cluttered environments. The state update step of the CJPDA filter (CJPDAF) is realized with the NNSF instead of Kalman filter. Through simulation, a comparison is made to show the performance difference between the CJPDA with NNSF (CJPDA-NNSF) proposed in this paper and the CJPDAF for different tracking scenarios. It was shown that the tracking performance of the proposed method is better than that of the CJPDAF

Artificial neural networks for calculating the association probabilities in multi-target tracking

A simple method based on the multilayered perceptron neural network architecture for calculating the association probabilities used in target tracking is presented. The multilayered perceptron is trained with the Levenberg-Marquardt algorithm. The tracks estimated by using the proposed method for multiple targets in cluttered and non-cluttered environments are in good agreement with the original tracks. Better accuracy is obtained than when using the joint probabilistic data association filter or the cheap joint probabilistic data association filter methods