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.

Vehicular fog computing (VFC) is an innovative computing paradigm with an exceptional ability to improve the vehicles’ capacity to manage computation-intensive applications with both low latency and energy consumption. Moreover, more and more Artificial Intelligence (AI) technologies are applied in task offloading on the Internet of Vehicles (IoV). Focusing on the problems of computing latency and energy consumption, in this paper, we propose an AI-based Vehicle-to-Everything (V2X) model for tasks and resource offloading model for an IoV network, which ensures reliable low-latency communication, efficient task offloading in the IoV network by using a Software-Defined Vehicular-based FC (SDV-F) architecture. To fit to time-critical data transmission task distribution, the proposed model reduces unnecessary task allocation at the fog computing layer by proposing an AI-based task-allocation algorithm in the IoV layer to implement the task allocation of each vehicle. By applying AI technologies such as reinforcement learning (RL), Markov decision process, and deep learning (DL), the proposed model intelligently makes decision on maximizing resource utilization at the fog layer and minimizing the average end-to-end delay of time-critical IoV applications. The experiment demonstrates the proposed model can efficiently distribute the fog layer tasks while minimizing the delay.

Edge feature extraction is an important basis part for subsequent processing of video frame. The region with a small color difference is transformed into a luminance value for edge detection, which increases the difficulty of edge detection and reduces the detection accuracy. In order to improve the precision of edge detection in the regions with small color difference, local autocorrelation is proposed to enhance the edge feature. Edge detection is completed by using chroma information, and the detection rate is used to measure the extent of the detected edge of the ideal edge to be extracted. The input video frame is converted to YCbCr color mode, and local autocorrelation is applied to the luminance information ${Y}$ and chroma information Cb for edge detection. Complementary processing is applied to the resulting image of edge detection based on local autocorrelation. In order to further improve the detection rate, supplementary processing is carried out. The standard edge images are defined for quantitative analysis and evaluation of proposed methods. Two kinds of edge correction processing based on the local autocorrelation comparison method and average method are proposed. Through experimental verification, the comparison method has a higher detection rate than the common method on the basis of local autocorrelation.