CAJ | 학술논문

文章根据头顶像素点的梯度方向具有固定范围的特性在前景中找出头顶候选点,依此快速确定人体头肩部区域,将其作为待测窗口;然后提取待测窗口的照度不变性色彩特征与旋转不变性 LBP纹理特征,并通过引入背景权重直方图算法(BWH)实现多特征融合;最后采用直方图交叉核支持向量机(HIKSVM)进行分类检测.实验结果表明,与传统的滑动窗口搜索方法相比,根据头顶点可以快速选取含有人体头肩部的待测窗口,提高了检测的效率; HSV和LBP多特征融合提高了检测的精确性,本文方法对于复杂动态场景、遮挡现象以及目标自身形变具有较强的鲁棒性和较高的准确性,在多种行人数据集中测试取得良好的效果.
문장근거두정상소점적제도방향구유고정범위적특성재전경중조출두정후선점,의차쾌속학정인체두견부구역,장기작위대측창구;연후제취대측창구적조도불변성색채특정여선전불변성 LBP문리특정,병통과인입배경권중직방도산법(BWH)실현다특정융합;최후채용직방도교차핵지지향량궤(HIKSVM)진행분류검측.실험결과표명,여전통적활동창구수색방법상비,근거두정점가이쾌속선취함유인체두견부적대측창구,제고료검측적효솔; HSV화LBP다특정융합제고료검측적정학성,본문방법대우복잡동태장경、차당현상이급목표자신형변구유교강적로봉성화교고적준학성,재다충행인수거집중측시취득량호적효과.
For the pixel gradient direction of the top of the head having a fixed scope, this paper firstly selects the candidate pixel points in the foreground. Then it locates the areas of human head-shoulder quickly by these points, which is defined as the windows to be tested. Secondly the illumination invariant color features and the rotation invariant LBP texture feature are extracted and combined together with the Background-weighted Histogram (BWH) algorithm. Lastly, Histogram Intersection Kernel Support Vector Machine (HIKSVM) classifies objects. Experimental results show that based on the pixel gradient direction of the top of the head, the windows which contain head-shoulder can be located more quickly than the traditional method, sliding window, which improves the efficiency of the detection. Furthermore, the accuracy of detection is also improved by the fusion feature of HSV and LBP. Experimental results show that the proposed algorithm is robust and accurate against cluttered dynamical background, occlusion and the object deformation, and tested in many pedestrian datasets and achieved good results.