Separation of Vehicle Detection Area Using Fourier Descriptor Under Internet of Things Monitoring

Abstract

With the popularity of automobiles, road traffic accidents and congestion have become increasingly serious. Therefore, technologies are needed to solve problems such as speeding and congestion. The detection and tracking of vehicles based on computer vision and Internet of Things monitoring are an important part of the intelligent traffic monitoring system. The angle between the camera and the vehicle will cause the gradually moving vehicles to have a connection during image segmentation. This paper aims to improve the detection accuracy of vehicles from camera images. A new separation method of the vehicle detection area was proposed in this paper. Moving areas are extracted by inter-frame differences, and vehicle areas are formed from the areas. If more than one vehicle area partially overlaps as one area, it is necessary to separate the area. The existing method extracts a place to be separated from an outline of the area. However, it is impossible for the method to separate vehicles using the extracted shape. Therefore, a new method is proposed that makes the place to be separated defined by the reshaping of the area with the use of the Fourier descriptor. The method tries to detect the place from the area. As a result, this method makes it possible to separate the area that the existing method cannot separate and it has obtained a high accuracy of separation in the experimental data of the Internet of Things monitoring.

Keywords

• Internet of Things monitoring
• vehicle area detection
• overlap segmentation
• Fourier descriptor
• Hough transformation
• precise segmentation