利用特征提取方法检测前方车辆

Using the Feature Extraction Method to Detect Vehicle Ahead

近年来,城市交通拥堵现象频现,交通事故频发,造成的人身伤亡、财产损失给社会生产带来严重的负面影响。因此,汽车主动安全技术的研究越来越受到关注。其中前方车辆检测技术能够根据装载在车上的传感器对车辆周围环境进行实时监测,避免因碰撞产生的交通事故,保障驾驶员安全。同时,对实现车辆自动驾驶,完善智慧交通建设,促进智慧城市发展有着重要的意义。首先结合各种传感器的特点,阐述选择视觉传感器进行车辆检测的原因,然后介绍视觉传感器特征提取方法检测前方车辆的方法,最后使用基于聚合通道特征(Aggregate Channel Features,ACF)提取的方法对前方车辆检测进行仿真验证。仿真结果表明,基于ACF特征提取的前方车辆检测技术提高了检测准确率,具有理论研究意义和工程应用价值。

In recent years,urban traffic congestion phenomenon frequency,frequent traffic accidents,resulting in personal casualties,property losses to social production has brought serious negative impact.Therefore,more and more attention has been paid to the research of vehicle active safety technology.The forward vehicle detection technology can monitor the environment around the vehicle in real time according to the sensors loaded on the vehicle to avoid traffic accidents caused by collisions and ensure driver safety.At the same time,it is of great significance to realize automatic driving of vehicles,improve the construction of intelligent transportation and promote the development of smart cities.First the reasons for choosing visual sensors for vehicle detection based on the characteristics of various sensors were described,and then the method of visual sensor feature extraction was introduced to detect vehicles in front.Finally,the method based on Aggregate Channel Features(ACF)extraction was used to simulate and verify the forward vehicle detection.The simulation results show that the forward vehicle detection technology based on ACF feature extraction improves the detection accuracy and has theoretical research significance and engineering application value.