Application of Image Processing and Three-Dimensional Data Reconstruction Algorithm Based on Traffic Video in Vehicle Component Detection

Abstract

Vehicle detection is one of the important technologies in intelligent video surveillance systems. Owing to the perspective projection imaging principle of cameras, traditional two-dimensional (2D) images usually distort the size and shape of vehicles. In order to solve these problems, the traffic scene calibration and inverse projection construction methods are used to project the threedimensional (3D) information onto the 2D images. In addition, a vehicle target can be characterized by several components, and thus vehicle detection can be fulfilled based on the combination of these components. The key characteristics of vehicle targets are distinct during a single day; for example, the headlight brightness is more significant at night, while the vehicle taillight and license plate color are much more prominent in the daytime. In this paper, by using the background subtraction method and Gaussian mixture model, we can realize the accurate detection of target lights at night. In the daytime, however, the detection of the license plate and taillight of a vehicle can be fulfilled by exploiting the background subtraction method and the Markov random field, based on the spatial geometry relation between the corresponding components. Further, by utilizing Kalman filters to follow the vehicle tracks, detection accuracy can be further improved. Finally, experiment results demonstrate the effectiveness of the proposed methods.

3D Indoor Scene Reconstruction and Layout Based on Virtual Reality Technology and Few-Shot Learning

“…The image method is also used in [ 19 ] to synthesize the model's facade, and the use of texture allows users to see the real street scene. A data-driven method for generating the room layout of the building plane was proposed in [ 20 ]. 120 architectural schemes were used in their study to train a Bayesian network to determine the relationship between rooms.…”