为降低夜间行车导致交通事故的概率,在能见度不理想的行车环境中为车辆提供主动安全系统,依据汽车辅助驾驶系统的基本要求,基于远红外传感技术设计出夜间行人辅助模型.该模型通过远红外传感器来捕捉原始的数据源,利用灰度统计技术获取候选区域(Regions of interest),构造出多尺度概率模板,在此基础上对数据源进行匹配检测,通过多帧校验综合处理技术进一步改善模型的漏检率和检测率.实验结果表明:该模型的概率模板在匹配精度上相对于业内常用方法有了较大程度的提高,能在郊区和市区交通路况下使用,实用性较好.
In order to reduce the probability of traffic accidents caused by night driving,and provide initiative safety system for vehicles in poor visibility environment,according to the basic requirements of auxiliary driving system,a nighttime pedestrian auxiliary model is designed based on far infrared sensor technology.The model captures the original data source by far infrared sensors.Regions of interest (ROIs) are obtained using gray statistical technique.Multi-scale probability template is constructed,and the data source is matched and detected.The detection rate and missing rate of the model are further improved by multi-frame checksum integrated processing technology.Experiment results show that compared to the common methods,the matching accuracy of probability template of the model is greatly improved.It can be used in the suburbs and urban traffic conditions,and its practicability is good.
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