Traffic monitoring is of major importance for enforcing traffic management policies. To ac-complish this task, the detection of vehicle can be achieved by exploiting image analysis techniques. In this paper, a solution is presented to obtain various traffic parameters through vehicular video de-tection system(VVDS). VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time. This algorithm uses the background differencing method, and vehicles can be detected through luminance difference of pixels between background image and current image. Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy. Then a hardware implementation of a digital signal pro-cessing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP's high performance. In the end, VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation.

Traditional methods of license character extraction cannot meet the requirements of recog-nition accuracy and speed rendered by the video vehicular detection system. Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algo-rithm based on Markov random field model is presented. Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold. The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%. When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system's requirement even under the noisy cir-cumstance or uneven illumination.

The resource of image noise is analysized in this paper. Considering the image fuzzy generated in the process of image denoising in spatial field, the image denoising method based on wavelet neural network with orthonormal bases is elaborated. The denoising principle and construction method of orthonormal wavelet network is described. In the simulation experiment, median filtering, adaptive median filtering and sym wavelet neural network with orthonormal bases were used separately in the denoising for contaminated images. The experiment shows that, compared with traditional denoising method, image denoising method based on orthonormal wavelet neural network improves greatly the image quality and decreases the image ambiguity.