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。%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.
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