In the advanced assisted driving device, machine vision technology was used to process the video of vehicles in front in real time to dynamically recognize and predict the posture and behavior of vehicle. Concerning low precision and large delay of this kind of recognition algorithm, a deep learning algorithm for vehicle behavior dynamic recognition based on Long Short-Term Memory (LSTM) was proposed. Firstly, the key frames in vehicle behavior video were extracted. Secondly, a dual convolutional network was introduced to analyze the feature information of key frames in parallel, and then LSTM network was used to sequence the extracted characteristic information. Finally, the output predicted score was used to determine the behavior type of vehicle. The experimental results show that the proposed algorithm has an accuracy of 95.6%, and the recognition time of a single video is only 1.72 s. The improved dual convolutional network algorithm improves the accuracy by 8.02% compared with ordinary convolutional network and increases by 6.36% compared with traditional vehicle behavior recognition algorithm based on a self-built dataset.
Aiming at false segmentation of small regions and high computational complexity in traditional color image segmentation algorithm, a hierarchical method of color image segmentation based on rough set and HIS (Hue-Saturation-Intensity) space was proposed. Firstly, for the reason that the singularities in HSI space are the achromatic pixels in RGB space, the achromatic regions of RGB space were segmented and labeled in order to remove the singularities from the original image. Secondly, the original image was converted from RGB space to HSI space. In intensity component, in view of spatial neighbor information and regional distribution difference, the original histogram was weighted by homogeneity function with changing thresholds and gradience. The weighted and original histograms were respectively used as the upper and lower approximation sets of rough set. The new roughness function was defined and applied to image segmentation. Then the different regions obtained in the previous stage were segmented according to the histogram in hue component. Finally, the homogeneous regions were merged in RGB space in order to avoid over-segmentation. Compared with the method based on rough set proposed by Mushrif etc. (MUSHRIF M M, RAY A K. Color image segmentation: rough-set theoretic approach. Pattern Recognition Letters, 2008, 29(4): 483-493), the proposed method can segment small regions easily, avoid the false segmentation caused by the correlation between RGB color components, and the executing speed is 5-8 times faster. The experimental results show the proposed method yields better segmentation, and it is efficient and robust to noise.