针对行人被障碍物部分遮挡导致的检测准确率降低问题,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.使用简单线性迭代聚类(simple linear iterative clustering,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;使用降维梯度直方图特征(histogram of gradient,HOG)进行特征提取并生成样本集,训练支持向量机(support vector machines,SVM)分类器,最终实现部分遮挡的行人检测.实验表明,所提算法与其他行人检测算法相比,在行人部分遮挡场景下,有着更高的行人检测准确率,证明所提算法的有效性.
Pedestrian Detection Method in Front of Vehicle Based on Binocular Vision
An algorithm for detecting partially occluded pedestrians based on multi-feature fusion and tree-structured semi-global stereo matching was proposed to address the issue of reduced detection accuracy in pedestrian detection caused by partial obstruction.The simple linear iterative clustering(SLIC)algorithm was employed for superpixel segmentation to enhance the contour information of pedestrians,and the tree-structured multi-feature fusion semi-global stereo matching algorithm was used to generate depth maps.Pedestrian,background,and obstacle information were separated using an adaptive segmentation algorithm to obtain the region of interest.The region of interest was positioned around the head and shoulders of the pedestrian,where features were distinct and stable,to impose constraints.Feature extraction was conducted using dimension-reduced histogram of gradient(HOG),and a sample set was generated for training an support vector machines(SVM)classifier,ultimately achieving the detection of partially occluded pedestrians.The experiment shows that compared with other pedestrian detection algorithms,the proposed algorithm has a higher accuracy in pedestrian detection in partially occluded scenes,proving the effectiveness of the proposed algorithm.
partially occluded pedestrian detectionsuperpixel segmentationstereo matchingregion of interestfeature extractionSVM classifier
万方数据
维普
