Smart monitoring, particularly at intersections, is a promising service that is being considered for the concept of smart cities. A network of light detection and ranging (LIDAR) sensors, which generates point cloud data in real time, can be used to detect people's mobility in smart monitoring. Due to the sheer volume of point cloud data, data transmission requires a significant amount of communication resources. In order to monitor people's mobility in real time, it is necessary to reduce the amount of transmission data to shorten delay. Point cloud compression is one method for reducing the amount of data. However, prior works addressing point cloud compression mainly focused on accuracy for the compression of an entire point cloud without considering its spatial characteristics. The more dynamically a spatial region changes, the more important it is when detecting moving objects such as cars, trucks, pedestrians, and bikes in smart monitoring. This paper proposes a prioritized transmission scheme that applies multiple point cloud compression methods to point cloud data according to the spatial importance of the data, i.e., how dynamically spatial regions change. This paper assumes data transmission of point cloud data from multiple LIDAR devices to an edge server and addresses the intra-frame geometry compression of point cloud data. The proposed scheme splits the point cloud into multiple classes according to the spatial importance and applies multiple point cloud compression methods to each class. A numerical study using a real point cloud dataset obtained at an intersection demonstrates the dependencies of quality, volume, and processing time on possible compression format options. The results verify that the proposed scheme reduces the amount of point cloud data drastically while satisfying the quality and processing time requirements.
ASJC Scopus subject areas
- コンピュータ サイエンス（全般）