Wei Yeang Kow

Universiti of Malaysia Sabah

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Kenneth Tze Kin Teo

Universiti of Malaysia Sabah

Wei Leong Khong

Monash University Malaysia

Yit Kwong Chin

Universiti Sains Malaysia

Ismail Saad

Universiti of Malaysia Sabah

Lorita Angeline

Universiti of Malaysia Sabah

Mei Yeen Choong

Universiti of Malaysia Sabah

Aroland Kiring

Universiti of Malaysia Sabah

Kin Teo

Universiti of Malaysia Sabah

Shahrum Shah

Universiti of Malaysia Sabah

Kenneth Tze

Universiti of Malaysia Sabah

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Universiti of Malaysia Sabah

300

Universiti Teknologi MARA

Universiti Sains Malaysia

University of Technology Malaysia

National University of Malaysia

Universiti Teknologi MARA System

University of Malaya

Hospital Universiti Sains Malaysia

Universiti Malaysia Perlis

Universiti Putra Malaysia

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Enhancement of Markov Chain Monte Carlo Convergence Speed in Vehicle Tracking Using Genetic Operator

Wei Yeang Kow Wei Leong Khong Yit Kwong Chin Ismail Saad Kenneth Tze Kin Teo

Markov Chain Monte Carlo (MCMC) has been essential in tracking vehicle undergoing disturbances for traffic surveillance purposes. It is capable of tracking vehicle by estimating the vehicle's position with the sampling of probability distributions. However the accuracy of the position estimation is highly dependent on the sampling efficiency of MCMC. Therefore the sample size of the MCMC is adapted to track the vehicle according to the disturbances encountered. The adaptive sample size of MCMC is determined by using the CUSUM path plot and variance ratio convergence diagnostic algorithm. To further enhance the convergence speed, genetic crossover and mutation operator is introduced into the adaptive MCMC. The genetic operator (GO) is capable of reduces the variance between samples and hence allowing faster convergence speed on the MCMC samples. Experimental results have shown that the GO adaptive MCMC tracking algorithm have better tracking performances with consumption of lesser sample size.

10.1109/cimsim.2012.61

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Citations (2)

Enhancement of Particle Filter Approach for Vehicle Tracking Via Adaptive Resampling Algorithm

Wei Leong Khong Wei Yeang Kow Farrah Wong Ismail Saad Kenneth Tze Kin Teo

Nowadays, vehicle tracking is a vital approach to assist and improve the road traffic control, surveillance and security systems by having the detail of the captured vehicle information. In past, many tracking techniques have been implemented and suffered from the well known 'occlusion' problems. Increasing the accuracy of the tracking algorithm has caused the computational cost due to the inflexibility to adapt the partial and fully occluded situations. Besides occlusion, appearance of new objects and background noises in the captured videos increase the difficulties of continuously tracking the labelled vehicles. In this paper, an adaptive particle filter approach has been proposed as the tracking algorithm to solve the vehicle occlusion problem. In order to solve the common particle filter degeneracy problem, the proposed particle filter is equipped with the adaptive resampling algorithm which is capable of dealing with various occlusion incidents. The experimental results show that enhancement of the particle filter via resampling algorithm has been robustly tracking the vehicles, and significantly improve the accuracy in tracking the occluded vehicles without compromising the processing time.

Resampling

Tracking (education)

Auxiliary particle filter

Degeneracy (biology)

10.1109/cicsyn.2011.62

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Citations (10)

License Plate Character Recognition via Signature Analysis and Features Extraction

Lorita Angeline Wei Yeang Kow Wei Leong Khong Mei Yeen Choong Kenneth Tze Kin Teo

A new algorithm for license plate character recognition is proposed on the basis of Signature analysis properties and features extraction. Signature analysis has been used to locate license plate region and its properties can be further utilised in supporting and affirming the license plate character recognition. This paper presents the implementation of Signature Analysis combined with Features Extraction to form feature vector for each character with a length of 56. The recognition stage utilised this vector to be trained in a simple multi-layer feed-forward back-propagation neural Network with 56 inputs and 34 neurons in its output layer. The network is trained with both ideal and noisy characters. The results obtained show that the proposed system is capable to recognise both ideal and non-ideal license plate characters. The system also capable to tackle the common character declassification problems due to similarity in characters.

Signature (topology)

Similarity (geometry)

Feature (linguistics)

10.1109/cimsim.2012.66

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Citations (6)

Table of Contents

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (2023)

Chenyang Hao M Hemel Md. Arifur Rahman Bhuiyan T Reaz K Almohamad

Table (database)

10.1109/iicaiet59451.2023.10291650

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Citations (0)

Table of contents

IEEE International Conference on Control System, Computing and Engineering (2012)

Yasir Hashim Othman Sidek Ayman Ahmed Salwa Elramly Mohamed El

Table (database)

10.1109/iccsce.2012.6487104

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Overlapped Vehicle Tracking via Enhancement of Particle Filter with Adaptive Resampling Algorithm

International Journal of Simulation Systems Science & Technology (2020)

Wei Leong Khong Wei Yeang Kow Lorita Angeline Ismail Saad Kenneth Tze Kin Teo

Traffic surveillance and on-road security have elevated the demand of machine vision aided traffic control system. Through the modern video camera technology, vehicle tracking has become a vital approach to assist the on-road traffic systems. In the past, many tracking methods have been developed based on the detail and information extracted from the captured vehicle. However, conventional tracking system need to be improved since the background noises and sudden appear objects will increase the difficulties of continuously tracking the target vehicle. Hence, a particle filter algorithm with adaptive resampling approach has been proposed to overcome the vehicle occlusion problems. In addition, the proposed resampling approach can also be used to solve the common particle degeneracy problem. Experimental results show that the enhanced particle filter equipped with adaptive resampling algorithm is significantly improving the accuracy of the tracking process without compromising the processing time.

Resampling

Tracking (education)

Auxiliary particle filter

10.5013/ijssst.a.12.03.07

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Citations (6)

CUSUM-Variance Ratio based Markov Chain Monte Carlo algorithm in overlapped vehicle tracking

Wei Yeang Kow Wei Leong Khong Yit Kwong Chin Ismail Saad Kenneth Tze Kin Teo

Markov Chain Monte Carlo (MCMC) is one of the algorithms that have been widely implemented in tracking vehicle for traffic surveillance purposes. The sampling efficiency of the algorithm is essential to determine the vehicle position accurately. However, the sample size of the algorithm is still remaining an issue as non-optimal sample size will defect the tracking accuracy, especially when the moving vehicle is overlapped. Adaptive sample size of MCMC has been implemented using CUSUM Path Plot and Variance Ratio algorithms to perform vehicle tracking. CUSUM Path Plot determines the samples convergence rate by calculating the hairiness of the sample size whereas Variance Ratio method computes two sets of MCMC to determine the samples steady state. This paper proposes the fusion of CUSUM-Variance ratio algorithm to enhance the tracking efficiency. Experimental results shows that the CUSUM-Variance Ratio method have a better performance in tracking the overlapping vehicle with higher accuracy and more optimal sample size compared to the standalone CUSUM Path Plot and Variance Ratio approaches.

CUSUM

Sample variance

Tracking (education)

10.1109/iccaie.2011.6162103

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Citations (5)

Image segmentation via normalised cuts and clustering algorithm

IEEE International Conference on Control System, Computing and Engineering (2012)

Mei Yeen Choong Wei Yeang Kow Yit Kwong Chin Lorita Angeline Kenneth Tze Kin Teo

Image segmentation has been widely applied in image analysis for various areas such as biomedical imaging, intelligent transportation systems and satellite imaging. The main goal of image segmentation is to simplify an image into segments that have a strong correlation with objects in the real world. Homogeneous regions of an image are regions containing common characteristics and are grouped as single segment. One of the graph partitioning methods in image segmentation, normalised cuts, has been recognised producing reliable segmentation result. To date, normalised cuts in image segmentation of various sized images is still lacking of analysis of its performance. In this paper, segmentation on synthetic images and natural images are covered to study the performance and effect of different image complexity towards segmentation process. This study gives some research findings for effective image segmentation using graph partitioning method with computation cost reduced. Because of its cost expensive and it becomes unfavourable in performing image segmentation on high resolution image especially in online image retrieval systems. Thus, a graph-based image segmentation method done in multistage approach is introduced here.

Segmentation-based object categorization

Range segmentation

Region growing

Cut

Connected-component labeling

10.1109/iccsce.2012.6487184

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Citations (30)

Kalman filtering based object tracking in surveillance video system

W. L. Khoong Wei Yeang Kow Hanlin Tan Hayoung Yoong Kenneth Tze Kin Teo

In the field of motion estimation for surveillance video, various techniques have been applied. One of the common approaches is Kalman filtering technique and it is interesting to explore the extension of this technique for the prediction and estimation of motion via the image sequences. In this paper, a moving object tracking in surveillance video using Kalman filter is proposed. The typical Kalman filter is good in tracking the position of a moving object. However, when dealing with occlusion, the typical Kalman filter is not able to keep tracking and predicting the position of the occluded moving object. During occlusion, the information of moving object is not available for detection and tracking. The lacking of occlusion scene determination and prediction ability cause the existing Kalman filter fails in tracking occluded object. Besides that, in the case of tracking multiple moving objects, existing Kalman filter will experience difficulties to identify the respective objects. Therefore, in order to encounter these problems, an object tracking method using enhanced Kalman filter will be developed. The ability of tracking occluded moving object will be added to increase the efficiency during tracking. Furthermore, object recognition feature will be added too to increase the accuracy of the object tracking system.

Tracking (education)

Tracking system

Alpha beta filter

Source

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Citations (6)

Multi-Area Load Frequency Control Using Particle Swarm Optimization

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (2023)

Min Keng Tan Wei Han Lim Kit Guan Lim Wei Yeang Kow Min Sian Ng

Load Frequency Control is indeed important in any country that operates an interconnected power grid for regulating system frequency by adjusting the generation to match the load demand. To maintain the power system stability and improve efficiency in scheduling, this paper proposes Two-area Load Frequency Control (LFC) Optimization with Particle Swarm Optimization. The design process begins with understanding the fundamental concepts, starting from Automotive Generation Control in a single-area power system design and progressing to LFC design in a two-area power system. The two-area power system consists of Tie-line Bias Control (TLBC) for maintaining zero tie-line power and Area Control Error (ACE) for stabilizing system frequency. To achieve fitness results in simulations, this paper presents three modeling designs: conventional design, controller design, and controller design with PSO. The controller system with PSO leads an evaluation of 53.4 % time schedule in system frequency performance and an evaluation of 76.9% time schedule in generation power deviation with respect to the conventional model. In addition, the controller system lags an evaluation of 70.0% time schedule in system frequency performance and an evaluation of a 78.7 ^% time schedule in generation power deviation with respect to the conventional model.

Automatic Generation Control

Frequency deviation

10.1109/iicaiet59451.2023.10291574

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