Vehicle video surveillance system based on image fusion and parallel computing

Abstract

Summary Autonomous driving has gradually moved towards practical applications in recent years. It is particularly critical to provide reliable real‐time environmental information for autonomous driving systems. At present, vehicle video surveillance systems based on multi‐source video and target detection algorithms can effectively solve these problems. However, the previous vehicle video surveillance systems are often unable to balance the surveillance effect and the surveillance frame rate. Therefore, we will introduce a vehicle video surveillance system based on parallel computing and computer vision in this article. First, multiple fisheye cameras are used to collect surround‐view environmental information. Second, we will use a low‐light camera, infrared thermal imager, and millimeter‐wave radar to provide forward‐view environmental information at night. Correspondingly, we designed the surround‐view image fusion algorithm and the forward‐view image fusion algorithm based on parallel computing. At last, a monocular camera and detection algorithms are used to provide forward‐view detection results. In a word, this vehicle video surveillance system will benefit the practical application of autonomous driving.

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

“…Many automakers have installed visible light cameras in their generated vehicles to detect obstacles, such as the parking assist systems in some Audi, Volkswagen, and Toyota models. Due to the visible camera at night, under low visibility conditions, such as fog effect is not ideal for target detection, in order to overcome the deficiency of visible light, infrared night vision assistant system research more and more, the night vision system based on infrared sensor has many advantages [33][34][35], embodied in the following aspects: (1) Infrared night vision system receives infrared radiation imaging of external targets and does not depend on the lighting conditions of the scene. Any object whose temperature is higher than absolute zero will radiate infrared.…”

10.4: A panoramic video processing algorithm based on variable surface projection

“…Taking the vehicle panoramic display system as an example, we hope that the final panoramic video image generated is of high quality and the stitching trace is not obvious. In the traditional processing methods as follows, [1] designed the surround view image fusion algorithm, which processed the original image of the six-channel fisheye camera and obtained the panoramic bird 's-eye image at the same time. [2] used bilinear interpolation algorithm to optimize the SPP algorithm based on scan line to realize image correction and effectively remove the serious distortion of the fisheye lens.…”