King Hann Lim

Lane Detection and Kalman-Based Linear-Parabolic Lane Tracking

This paper presents a lane detection and linear-parabolic lane tracking system using kalman filtering method. First, the image horizon is detected in a traffic scene to split the sky and road region. Road region is further analyzed with entropy method to remove the road pixels. Lane boundaries are then extracted from the region using lane markings detection. These detected boundaries are tracked in consecutive video frames with a linear-parabolic tracking model. The model parameters are updated with Kalman filtering method. Error-checking is performed iteratively to ensure the performance of the lane estimation model. Simulation results demonstrate good performance of the proposed Kalman-based linear-parabolic lane tracking system with fine parameters update.

Intra color-shape classification for traffic sign recognition

This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural network (RBFNN). In the proposed system, traffic signs are first segmented and classified with regard to its unique color and shape in order to partition a large set of data into smaller subclasses. Within these subclasses, all redundant information except the pictogram is discarded for feature selection since the pictogram contains critical information for road users. Principle Component Analysis (PCA) is applied to extract salient points for traffic sign dimensionality reduction. This is followed by the Fishers Linear Discriminant (FLD) to further obtain the most discriminant features. These features are fed into RBFNN for training with a proposed weight updating scheme based on Lyapunov stability theory. The performance of the proposed system is evaluated with Malaysian road signs with promising recognition rate.

Lyapunov Theory-Based Multilayered Neural Network

This brief presents a Lyapunov theory-based weight adaptation scheme for a multilayered neural network (MLNN) mainly used to classify a multiple-input-multiple-output (MIMO) problem. Initially, the MLNN system is linearized using Taylor series expansion. Then, the weight adaptation scheme is designed based on the Lyapunov stability theory to iteratively update the weight. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Hence, the Lyapunov theory-based MLNN acts as a MIMO classifier for face recognition. Analysis and discussion on Lyapunov properties of the proposed classifier are included. The performance of the proposed technique is tested on the Olivetti Research Laboratory database for face classification, and some comparisons with existing conventional techniques are given. Simulation results have revealed that our proposed system achieved better performance.

Malaysia traffic sign recognition with convolutional neural network

Traffic sign recognition system is an important subsystem in advanced driver assistance systems (ADAS) that assisting a driver to detect a critical driving scenario and subsequently making an immediate decision. Recently, deep architecture neural network is popular because it adapts well in various kind of scenarios, even those which were not used during training. Therefore, a deep architecture neural network is implemented to perform traffic sign classification in order to improve the traffic sign recognition rate. A comparative study for a deep and shallow architecture neural network is presented in this paper. Deep and shallow architecture neural network refer to convolutional neural network (CNN) and radial basis function neural network (RBFNN) respectively. In the simulation result, two types of training modes had been compared i.e. incremental training and batch training. Experimental results show that incremental training mode trains faster than batch training mode. The performance of the convolutional neural network is evaluated with the Malaysian traffic sign database and achieves 99% of the recognition rate.

MIMO Lyapunov theory-based RBF neural classifier for traffic sign recognition

Lyapunov theory-based radial basis function neural network (RBFNN) is developed for traffic sign recognition in this paper to perform multiple inputs multiple outputs (MIMO) classification. Multidimensional input is inserted into RBF nodes and these nodes are linked with multiple weights. An iterative weight adaptation scheme is hence designed with regards to the Lyapunov stability theory to obtain a set of optimum weights. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Weight gain is formed later to obey the Lyapunov stability theory. Detail analysis and discussion on the proposed classifiers properties are included in the paper. The performance comparisons between the proposed classifier and some existing conventional techniques are evaluated using traffic sign patterns. Simulation results reveal that our proposed system achieved better performance with lower number of training iterations.

Real-Time Implementation of Vision-Based Lane Detection and Tracking

This paper presents a real-time implementation on lane detection and tracking system in order to localize lane boundaries and estimate a linear-parabolic lane model. It is realized using TMS320DM642 DSP board. Video frame is first captured with CCD camera and stored in video port buffer. Next, input image is split into sky and road region with horizon localization. Lane analysis is applied on the road region to remove road pixels. Only lane markings are the interests for the lane detection process. Once lane boundaries are located, the possible edge pixels are scanned to continuously obtain the lane model. Linear-parabolic model is used to construct the geometry of the lane. The model parameters are updated with Kalman filtering. Video sequences are tested to verify the performance of the system and it has good performance.

New hybrid technique for traffic sign recognition

A hybrid traffic sign recognition scheme combining of knowledge-based analysis and radial basis function neural classifier (RBFNN) is proposed in this paper. Initially, traffic signs are detected from the road scenes using color segmentation method. The extracted signs are then passed to the recognition system for classification. The proposed recognition technique composes of three stages: (i) color histogram classification, (ii) shape classification and, (iii) RBF neural classification. Based on the unique color and shape of traffic signs, they can be classified into smaller subclasses and can be easily recognized using RBFNN. Before feeding traffic sign into the RBFNN, traffic sign features are extracted by Principle Component Analysis (PCA) in order to reduce the dimensionality of the original images. This is followed by the Fishers Linear Discriminant (FLD) to further obtain the most discriminant features. The performance of the proposed hybrid system is evaluated and compared to the purely neural classifier. The experimental results demonstrate that the proposed method has better recognition rate.

Multilayer perceptrons neural network based Web spam detection application

Web spam detection is a crucial task due to its devastation towards Web search engines and global cost of billion dollars annually. For these reasons, a multilayered perceptrons (MLP) neural network is presented in this paper to improve the Web spam detection accuracy. MLP neural network is used for Web spam classification due to its flexible structure and non-linearity transformation to accommodate latest Web spam patterns. An intensive investigation is carried out to obtain an optimal number of hidden neurons. Both Web spam link-based and content-based features are fed into MLP network for classification. Two benchmarking datasets - WEBSPAM-UK2006 and WEBSPAM-UK2007 are used to evaluate the performance of the proposed classifier. The overall performance is compared with the state of the art support vector machine (SVM) which is widely used to combat Web spam. The experiments have shown that MLP network outperforms SVM up to 14.02% on former dataset and up to 3.53% on later dataset.

River flow lane detection and Kalman filtering-based B-spline lane tracking

A novel lane detection technique using adaptive line segment and river flow method is proposed in this paper to estimate driving lane edges. A Kalman filtering-based B-spline tracking model is also presented to quickly predict lane boundaries in consecutive frames. Firstly, sky region and road shadows are removed by applying a regional dividing method and road region analysis, respectively. Next, the change of lane orientation is monitored in order to define an adaptive line segment separating the region into near and far fields. In the near field, a 1D Hough transform is used to approximate a pair of lane boundaries. Subsequently, river flow method is applied to obtain lane curvature in the far field. Once the lane boundaries are detected, a B-spline mathematical model is updated using a Kalman filter to continuously track the road edges. Simulation results show that the proposed lane detection and tracking method has good performance with low complexity.

A new multi-purpose audio-visual UNMC-VIER database with multiple variabilities

Audio-visual recognition system is becoming popular because it overcomes certain problems of traditional audio-only recognition system. However, difficulties due to visual variations in video sequence can significantly degrade the recognition performance of the system. This problem can be further complicated when more than one visual variation happen at the same time. Although several databases have been created in this area, none of them includes realistic visual variations in video sequence. With the aim to facilitate the development of robust audio-visual recognition systems, the new audio-visual UNMC-VIER database is created. This database contains various visual variations including illumination, facial expression, head pose, and image resolution variations. The most unique aspect of this database is that it includes more than one visual variation in the same video recording. For the audio part, the utterances are spoken in slow and normal speech pace to improve the learning process of audio-visual speech recognition system. Hence, this database is useful for the development of robust audio-visual person, speech recognition and face recognition systems.