物联网下大区域校园智能视觉特征定位技术仿真

为了解决传统技术易受外界干扰，造成视觉特征存在缺失，影响定位结果，且仅可应用于颜色特征显著的视觉特征定位的弊端，通过SURF法和Euler距离匹配研究了一种物联网下大区域校园智能视觉特征定位技术。通过物联网技术对监控的大区域校园图像进行智能采集，给出物联网视觉传感器分布情况。针对采集图像进行预处理，增强图像干扰抑制能力。把图像当成包，把分割后图像块当成包中的示例，在大区域内为某视觉图像确定最优标注。在此基础上，通过SURF算法对视觉特征点进行检测，利用Euler距离实现物联网下大区域校院智能视觉特征匹配定位。结果表明：所提技术检测特征无显著差异，具有不变性；对白天校园人行道区域进行视觉特征定位，定位误差低；对夜间校园主干道区域进行视觉特征定位，定位误差较白天无显著差异。可见所提技术视觉特征定位精度高。

Simulation of intelligent visual location technology for large area Campus under the Internet of things

In order to solve the problem that traditional technology is susceptible to external disturbance, resulting in the absence of visual features, affecting the localization results, and can only be applied to visual feature localization with significant color features, a large area campus intelligent visual feature localization technology based on Internet of Things (IOT) is studied by SURF and Euler distance matching. Intelligent acquisition of the campus image of the large area monitored by the Internet of Things (IOT) technology is carried out, and the distribution of the IOT vision sensor is given. Preprocessing the acquired images to enhance the ability of image interference suppression. The image is regarded as a packet, and the segmented image block is regarded as an example in the packet to determine the optimal labeling for a visual image in a large area. On this basis, SURF algorithm is used to detect the visual feature points, and Euler distance is used to realize the intelligent visual feature matching and location of large regional college under the Internet of Things. The results show that there is no significant difference in the detection characteristics of the proposed technology, and the positioning error is low for the daytime campus sidewalk area, and no significant difference is found for the night campus main road area. It can be seen that the positioning accuracy of the proposed technology is high.