Moving vehicle detection and tracking is the key technology in the intelligent traffic monitoring system. For the shortcomings and deficiencies of the frame-subtraction method, a novel Marr wavelet, kernel-based background modeling method and a background subtraction method based on binary discrete wavelet transforms (BDWT) are introduced. The background model keeps a sample of intensity values for each pixel in the image and uses this sample to estimate the probability density function of the pixel intensity. The density function is estimated using a new Marr wavelet kernel density estimation technique. The background and current frame are transformed by BDWT, and moving vehicles are detected in the binary discrete wavelet transforms domain. For the shortages of RGB (Red, Green, Blue) or HSV (Hue, Saturation, Value) color space-based vehicle shadow segmentation algorithms, shadow segmentation algorithm based on YCbCr color space and edge detection is proposed. The original data of the shadow according to the characteristics of the YCbCr space is chosen, and then, combined with edge detection, the shape and location of the vehicle region is determined. An automatic particle filtering algorithm is used to track the vehicle after detection and obtaining the center of the object. An actual road test shows that the algorithm can effectively remove the influence of pedestrians and cyclists in the complex environment, and can track the moving vehicle exactly. The algorithm with better robustness has a practical value in the field of intelligent traffic monitoring.

This paper presents a new fuzzy cover-based clustering algorithm. In the proposed algorithm, the concept of fuzzy cover and objective function are employed to identify holding points in the dataset, and we associate these holding points together to build up the backbones of the final clusters. Three specific objectives underlie the presentation of the proposed approach in this paper. The first is to describe mathematical formulation of the fuzzy covers, and the second is to summarize the detailed procedure of constructing fuzzy covers and splicing them into clusters. The third goal is to demonstrate that this approach is able to find out reasonable representative patterns in the final clusters. We illustrate this approach with four examples in order to verify the clustering effectiveness.