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、SIFT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。
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.
出处
《电视技术》
北大核心
2016年第3期111-115,145,共6页
Video Engineering
基金
国家自然科学基金项目(61379130)
