中文摘要：

分析公路视频图像，从而对经过的车辆进行较高精度的分类，是一个颇且实用价值的课题。如何在保证分类精度的同时提高系统性能，无疑是一个具有挑战性的任务。提出了一个多特征融合的分类框架，结合车辆的全局几何特征、SIFY局部特征，以及Gabor纹理特征对车辆进行分类，提高了分类精度；为了提高系统的性能，设计了基于MapReduce的并行算法，通过对图像分块，实现数据并行。实验结果表明，该方案能够在提高分类精度的基础上仍然保持较高的系统性能。

英文摘要：

Vehicle classification with a higher accuracy based on highway video, using image analysis technology is a very valuable subject. However, how to improve system performance while ensuring the classification accuracy is undoubtedly a challenging task. Since both global features and local features are essential to classification, a multi-feature fusion classification framework is presented, which combines with global geometric features, SIFT, and Gabor local features to improve the classification accuracy. In order to improve system performance, a parallel algorithm based on MapReduce programming model is designed. Experimental results show that this scheme can improve the classification accuracy and still maintain a high system performance.