基于双目视觉的部分遮挡行人检测算法

针对行人被障碍物部分遮挡导致的检测准确率降低问题,本文提出了基于多特征融合的树形路径半全局立体匹配的部分遮挡行人检测算法。本方法使用SLIC算法进行超像素分割,提升行人的轮廓信息,并使用多特征融合的树形路径半全局立体匹配算法生成深度图;对行人信息和背景信息及障碍物信息使用自适应分割算法进行分离,获取感兴趣区域;将感兴趣区域放置在行人特征明显且稳定的头肩部,进行感兴趣区域的约束;使用降维HOG进行特征提取并生成样本集,训练SVM分类器,最终实现部分遮挡的行人检测。实验表明,本文算法与其它行人检测算法相比,在行人部分遮挡场景下,有着更高的行人检测准确率,证明本文算法的有效性。

Research on pedestrian detection method in front of vehicle based on binocular vision

In order to reduce the detection accuracy of pedestrians partially blocked by obstacles, this thesis proposes a semi-global stereo matching algorithm for partially blocked pedestrians based on multi-feature fusion. In this method, SLIC algorithm is used for super-pixel segmentation to improve pedestrian contour information, and multi-feature fusion tree path semi-global stereo matching algorithm is used to generate depth map. The pedestrian information, background information and obstacle information are separated by adaptive segmentation algorithm to obtain the area of interest. The area of interest was placed on the head and shoulders with obvious and stable pedestrian features to restrict the area of interest. Dimension reduction HOG is used to extract features and generate sample sets, and SVM classifier is trained to realize partially occlusioned pedestrian detection. Experiments show that compared with other pedestrian detection algorithms, the proposed algorithm has a higher pedestrian detection accuracy in the scene of partially occluded pedestrians, which proves the effectiveness of the proposed algorithm.