基于表面深度值均方差的航空行李分类研究

以航空旅客行李托运方式的国际标准为出发点,研究了基于行李表面深度值均方差的分类方法,为采取何种方式托运提供依据。采用Kinect传感器在行李输送带上方采集深度图像,提取行李区域的像素值并计算其均方差进行粗分类;结合行李三维形态的先验知识,根据网格之间的距离以及深度值均方差的差异,设计了基于网格相似度的自适应聚类算法,拟合聚类结果中高层单元的面积和数量,分析行李三维形态特征,确定其类别。实验结果表明,所提算法复杂度低,能快速准确地识别行李类别。

Airline baggage classification based on the mean square error of the surface depth

According to the international standard of baggage consignment methods for airline passengers, we study the classification method based on the variance of the surface depth of the baggage, and attempt to provide research basis for consignment methods. The depth image above the baggage conveyor belt is acquired by the sensor of Kinect, and pixel values of the baggage area are extracted so as to calculate their variance to classify the baggage roughly. In combination with prior knowledge of baggage’s three dimensional surface morphology, and according to the distance between grids and the variance of the depth value’s difference, we design a self adaptive clustering algorithm based on grid similarity, which fits the clustering results of the area of each top unit as well as the number of all the top units. The three dimensional morphological characteristics of baggage are analyzed and the classification of baggage is determined. Experimental results indicate that this algorithm is of low complexity, and the classification is accurate and effective.