【摘要】 提出一种用于复杂光照环境下的车辆检测方法,该方法在传统的假设验证框架下充分利用了先验知识和复杂光照背景下的车辆特征.在假设生成阶段,利用车辆边缘信息与车辆前部形状特征进行拟合来生成假设;在假设验证阶段,使用HOG特征作为描述子,结合SVM分类器完成假设车辆目标的验证识别.实验结果表明在复杂的光照环境中,本文方法能够有效检测出传统方法无法检测的目标,是对正常光照环境下车辆检测方法的有效补充.更多还原

【Abstract】 An on-road vehicle detection method under complex illumination environments was introduced.The approach uses the features of a vehicle under complex illumination environment and prior knowledge of the vehicle’s front shape based on the hypothesis-verification framework.During the stage of hypothesis generation,edges were extracted from the front image of a vehicle and then fit approximately with the front shape of the vehicle.In the hypothesis verification phase,HOG features were used as a descriptor,in combination with the SVM classifier to complete the verification of hypothesis.The experimental results show that the proposed method works well in complex illumination environment,and it has good performance in detecting vehicle targets under complex illumination environment.更多还原