CAJ | 학술논문

针对传统车辆检测方法定位精度不高的问题，提出一种基于多特征融合的前向车辆检测方法。采用基于直方图分析和自适应双阈值的方法分别实现阴影和边缘特征的准确分割，并通过阴影和边缘特征的综合分析，生成车辆假设区域。利用对称性、纹理和轮廓匹配度3个特征融合得到的综合特征对获得的车辆假设区域进行验证，剔除其中的误检区域。实验结果证明，该方法能在不同光照条件下自适应地进行车辆检测，检测率可达92%以上，且在检测率和误检率2项指标上均优于传统基于学习的方法。
침대전통차량검측방법정위정도불고적문제，제출일충기우다특정융합적전향차량검측방법。채용기우직방도분석화자괄응쌍역치적방법분별실현음영화변연특정적준학분할，병통과음영화변연특정적종합분석，생성차량가설구역。이용대칭성、문리화륜곽필배도3개특정융합득도적종합특정대획득적차량가설구역진행험증，척제기중적오검구역。실험결과증명，해방법능재불동광조조건하자괄응지진행차량검측，검측솔가체92%이상，차재검측솔화오검솔2항지표상균우우전통기우학습적방법。
A forward vehicle detection method based on multi-feature fusion is proposed in order to improve the accuracy of vehicle detection. The shadow and edge features of vehicle are segmented accurately by using histogram analysis method and adaptive dual-threshold method respectively. The initial candidates are generated by combining edge and shadow features and these initial candidates are further verified by using an integrated feature based on the fusion of symmetry, texture and shape matching degree features. A threshold is used to remove the non-vehicle initial candidates. Experimental results show that this method can adapt to different light conditions robustly with a detection rate over 92%. The proposed method is better than traditional methods based on learning with a higher detection rate and lower error rate.