Aiming at the problem of tracking instability of the Lucas-Kanade (LK) algorithm for the complex situation of moving target deformation, fog and haze, high-speed, uneven illumination and partial occlusion in traffic monitoring, a tracking algorithm based on multi-resolution LK optical flow algorithm and Speed Up Robust Features (SURF) was proposed. The problem tracking failure for large-scale motion between frames of same pixel point in the traditional LK algorithm was solved by the proposed method, and the SURF scale invariant feature transformation algorithm was combined, feature points for optical flow tracking were extracted, and an adaptive template real-time update strategy was developed; the amount of optical flow calculation was reduced while enhancing the resistance ability of moving targets against complex environments. The experimental results show that the feature points matching of the new method is accurate and fast, which has strong adaptability and it is stable in the complicated traffic environment.

Concerning the fixed direction and monotony of lossless image compression template of Gradient Adjusted Predictor (GAP), according to the characteristic of the actual edge with the same linear increments, this paper proposed Multidirectional Linear Gradient Adjusted Predictor (MLGAP) template. Firstly the image was cut into four sub-images from the center to the periphery. With the application of Graphics Processor Unit (GPU) parallel technology operation, the predictive value was calculated by MLGAP template in each sub-image, and then error feedback was used to construct prediction error image. The threshold was calculated by OSTU algorithm, and error image edge was classified. At last the edge was thinned by Hilditch algorithm. The simulation results show that using the proposed method can get clear, complete and precise edges. In addition, the GPU parallel technology accelerates the image processing.

By using Gradient Adjusted Predictor (GAP) and Gradient Edge Detection (GED) predictors of lossless image encoding for reference, with an improved one, a new image edge detection algorithm of the dynamic threshold control based on Multidirectional Gradient Edge Detection Predictor (MGEDP) template was proposed. The image was cut into four equal parts, and these parts would be executed simultaneously by MGEDP template in different direction of four opposite ways to calculate the error values by the parallel technology. From these feedback values, the algorithm created error image, calculated the threshold values by Otsu algorithm, classified the edges of error image, thinned the edges, and composed the last edge image. The experimental results show that the algorithm using parallel technology not only decreases the time complexity, but also gets the clearer edges, more details, and better visual image.

To improve the bandwidth utilization of a FlexRay network, it is necessary to gain a deep insight into this performance parameter. Based upon current model for time parameter optimization in a FlexRay network, through reducing a nonlinear operator to a linear one, analytical expressions of the optimal payload length of static frame and the maximum bandwidth utilization in the static segment of a FlexRay network were derived. The numerical experiments verify the validity of the analytical expressions, and demonstrate that the obtained analytical formulae are capable of providing an exact calculation for the bandwidth utilization in a FlexRay network.