The rational layout of urban commercial space is conducive to optimizing the allocation of commercial resources in the urban interior space. Based on the commercial POI (Point of Interest) data in the central district...The rational layout of urban commercial space is conducive to optimizing the allocation of commercial resources in the urban interior space. Based on the commercial POI (Point of Interest) data in the central district of Mianyang, the characteristics of urban commercial spatial pattern under different scales are analyzed by using Kernel Density Estimation, Getis-Ord , Ripley’s K Function and Location Entropy method, and the spatial agglomeration characteristics of various industries in urban commerce are studied. The results show that: 1) The spatial distribution characteristics of commercial outlets in downtown Mianyang are remarkable, and show a multi-center distribution pattern. The hot area distribution of commercial outlets based on road grid unit is generally consistent with the identified commercial density center distribution. 2) The commercial grade scale structure has been formed in the central urban area as a whole, and the distribution of commercial network hot spots based on road grid unit is generally consistent with the identified commercial density center distribution. 3) From the perspective of commercial industry, the differentiation of urban commercial space “center-periphery” is obvious, and different industries show different spatial agglomeration modes. 4) The multi-scale spatial agglomeration of each industry is different, the spatial scale of location choice of comprehensive retail, household appliances and other industries is larger, and the scale of location choice of textile, clothing, culture and sports is small. 5) There are significant differences in specialized functional areas from the perspective of industry. Mature areas show multi-functional elements, multi-advantage industry agglomeration characteristics, and a small number of developing areas also show multi-advantage industry agglomeration characteristics.展开更多
针对在复杂场景下,聚合通道特征(ACF)的行人检测算法存在检测精度较低、误检率较高的问题,提出一种结合纹理和轮廓特征的多通道行人检测算法。算法由训练分类器和检测两部分组成。在训练阶段,首先提取ACF特征、局部二值模式(LBP)纹理特...针对在复杂场景下,聚合通道特征(ACF)的行人检测算法存在检测精度较低、误检率较高的问题,提出一种结合纹理和轮廓特征的多通道行人检测算法。算法由训练分类器和检测两部分组成。在训练阶段,首先提取ACF特征、局部二值模式(LBP)纹理特征和ST(Sketch Tokens)轮廓特征,然后对提取的三类特征均采用Real Ada Boost分类器进行训练;在检测阶段,应用了级联检测的思想,初期使用ACF分类器处理所有实例,保留下来的少数实例应用复杂的LBP及ST分类器进行逐次筛选。实验采用INRIA数据集对算法进行仿真,该算法的平均对数漏检率为13.32%,与ACF算法相比平均对数漏检率降低了3.73个百分点。实验结果表明LBP特征与ST特征能有对ACF特征进行信息互补,从而在复杂场景下去掉部分误判,提高了行人检测的精度,同时应用级联检测保证了多特征算法的计算效率。展开更多
文摘The rational layout of urban commercial space is conducive to optimizing the allocation of commercial resources in the urban interior space. Based on the commercial POI (Point of Interest) data in the central district of Mianyang, the characteristics of urban commercial spatial pattern under different scales are analyzed by using Kernel Density Estimation, Getis-Ord , Ripley’s K Function and Location Entropy method, and the spatial agglomeration characteristics of various industries in urban commerce are studied. The results show that: 1) The spatial distribution characteristics of commercial outlets in downtown Mianyang are remarkable, and show a multi-center distribution pattern. The hot area distribution of commercial outlets based on road grid unit is generally consistent with the identified commercial density center distribution. 2) The commercial grade scale structure has been formed in the central urban area as a whole, and the distribution of commercial network hot spots based on road grid unit is generally consistent with the identified commercial density center distribution. 3) From the perspective of commercial industry, the differentiation of urban commercial space “center-periphery” is obvious, and different industries show different spatial agglomeration modes. 4) The multi-scale spatial agglomeration of each industry is different, the spatial scale of location choice of comprehensive retail, household appliances and other industries is larger, and the scale of location choice of textile, clothing, culture and sports is small. 5) There are significant differences in specialized functional areas from the perspective of industry. Mature areas show multi-functional elements, multi-advantage industry agglomeration characteristics, and a small number of developing areas also show multi-advantage industry agglomeration characteristics.
文摘针对在复杂场景下,聚合通道特征(ACF)的行人检测算法存在检测精度较低、误检率较高的问题,提出一种结合纹理和轮廓特征的多通道行人检测算法。算法由训练分类器和检测两部分组成。在训练阶段,首先提取ACF特征、局部二值模式(LBP)纹理特征和ST(Sketch Tokens)轮廓特征,然后对提取的三类特征均采用Real Ada Boost分类器进行训练;在检测阶段,应用了级联检测的思想,初期使用ACF分类器处理所有实例,保留下来的少数实例应用复杂的LBP及ST分类器进行逐次筛选。实验采用INRIA数据集对算法进行仿真,该算法的平均对数漏检率为13.32%,与ACF算法相比平均对数漏检率降低了3.73个百分点。实验结果表明LBP特征与ST特征能有对ACF特征进行信息互补,从而在复杂场景下去掉部分误判,提高了行人检测的精度,同时应用级联检测保证了多特征算法的计算效率。
