摘要
针对智能交通系统中车辆检测对处理实时性和使用环境的要求,提出了一种基于动物视觉的车流量检测算法,并将算法封装成组件应用于异构处理平台中.此算法通过判别相邻像素点的关联性,消除伪误差,提高了车辆识别的准确率.通过更新优化处理组件,快速更新车辆检测处理算法,有助于新算法快速应用到实际中去.实验证明,利用异构处理平台更新算法的方式,缩短了算法开发周期,提高了算法利用率,可有效快速的对处理流程进行更新.
For the demands of real-time processing and application environment, a new animal vision-based vehicle identification algorithm is put forward, and packaged as component used in heterogeneous processing platform. By determining relevance of neighboring pixels, this algorithm eliminates false errors and raises the accuracy of vehicle identification. Meanwhile speediness algorithm adapting contributes to rapid application. Experimental results show that the heterogeneous processing platform reduces development period, improves algorithm utilization, fast updates algorithm and processing.
出处
《计算机系统应用》
2016年第10期268-272,共5页
Computer Systems & Applications
关键词
车辆检测
异构平台
智能交通系统
组件化
vehicle identification
heterogeneous platform
ITS
componentization
