Chaiwat Nuthong

A computer vision based vehicle detection and counting system

A vehicle detection and counting system plays an important role in an intelligent transportation system, especially for traffic management. This paper proposes a video-based method for vehicle detection and counting system based on computer vision technology. The proposed method uses background subtraction technique to find foreground objects in a video sequence. In order to detect moving vehicles more accurately, several computer vision techniques, including thresholding, hole filling and adaptive morphology operations, are then applied. Finally, vehicle counting is done by using a virtual detection zone. Experimental results show that the accuracy of the proposed vehicle counting system is around 96%.

Frequency component analysis of eeg recording on various visual tasks: Steady-state visual evoked potential experiment

Nowadays, steady-state visual evoked potential (SSVEP) is ongoing in many research topics. It also plays an important role in the response to various visual stimuli such as flickering rate (F), intensity (I), and duty cycle (D). The SSVEPs are practical and useful in research because of its excellent signal-to-noise ratio and relative immunity to artifacts. The application of using SSVEP-based system has been widely succeeded in many disciplines. In this paper, we investigate SSVEP corresponding to the different visual stimulation in terms of frequency component analysis in the four principal frequency bands, i.e., delta (0.1-3.5 Hz), theta (4.0-7.5 Hz), alpha (8.0-13.0 Hz), and beta (14.0-30.0 Hz). Subjects were instructed to fixate LED light source then record associated SSVEP waveform. The variation in displaying of the presentation stimuli during a task was examined. Experimental results showed that the major frequency distribution has been found in theta and alpha bands.

Lane detection using smoothing spline

In vehicle safety research area, a Lane Departure Warning System (LDWS) is of a major interest. The system, however, needs lane detection and lane tracking as basis. This paper proposes an algorithm used to detect the lane as an initialization for lane tracking. The algorithm takes an image captured from a video stream as an input. It then segments the image into a number of small tiles and applies the Principle Component Analysis (PCA) to each tile in order to find the centroid and the principle axis. After recovering the original image from all of the tiles, the probable lines are found using k-means clustering techniques. Two out of these lines will be selected to be the lanes of the road. At the end, the corresponding centroid points are chosen to be represented as control points of smoothing splines represented the lane. This obtained splines are aimed to be used further as an initialization of the lane tracking algorithm. The method has been tested and worked fine. However, its limitation has also been stated and needed to be improved in the future.

Road feedback estimation for steer-by-wire control

Sensing represents a critical issue in the design of steer-by-wire systems. Specifically, the road feedback information resulting from the interaction between the tire, vehicle dynamics and road is important for the quality of force feedback. Its sensing is however related to disadvantages with respect to cost, robustness and failure issues. In this paper instead, the estimation strategies are proposed as favorable approach. The friction forces are modelled as stochastic Gauss-Markov process and an extended Kalman filter is designed for its estimation. This signal is further coupled to a steer-by-wire control setup. Simulation data with a non-linear vehicle dynamics model validate the applicability and efficiency of the approach.

Vehicle detection and classification system based on virtual detection zone

This paper proposes a vehicle detection and classification system based on virtual detection zone (VDZ). The proposed system consists of four main steps: foreground extraction, vehicle detection, vehicle feature extraction and vehicle classification. A moving vehicle is firstly detected based on Gaussian mixture model (GMM). Then, several techniques including region of interest selection, adaptive morphological operation, and contour processing are applied to obtain correct foreground objects. Next, vehicle features are calculated when the centroid of a vehicle is on the VDZ. Finally, vehicles are classified by using k-nearest neighbor classifier. Experimental results show that the proposed method can accurately detect and classify vehicles with an accuracy of 98.53%.

Stochastic Models for Tyre Force Estimation: An Overview about Recent Developments and Trends

Abstract Friction forces between the road and the tires of a vehicle represent a key technology for modern vehicle control systems. Unfortunately, these forces are difficult to measure, so that these signals have to be estimated by common sensors of series production vehicles (e.g. accelerations, wheel speeds and yaw rate) and tire friction models. A lot of research effort has been done in this area and various analytical and semi-empirical models are proposed in the literature. To reduce the corresponding identification efforts of several specific model parameters, stochastic tire models are proposed as an interesting alternative. In this article, an overview of recent developments and trends in stochastic tire modeling and estimation methods is given. Furthermore, some representive results of two tire-road friction estimation methods (extended Kalman filter (EKF) and unscented Kalman filter (UKF) with field test data are presented.