Vehicle detection using Gaussian mixture model for infrared orientation-code image

Abstract

This paper describes an approach to the detection of vehicles in infrared images. Stable vehicle detection is important for future intelligent transport systems and is generally done by background subtraction and object modeling. To avoid the daylight-dependent and weather-dependent influences of varying illumination in visible images acquired with conventional ITV cameras, some researchers have been using infrared (IR) images. IR images make it easy to extract foreground vehicle regions from background scenes, but their lack of clarity make object modeling difficult. We therefore propose a method that describes the internal pattern of each vehicle by using Gaussian mixture models (GMM) in the orientation-code image (OCI) space. Each pixel of an OCI has information about the maximum-gradient orientation of the IR image, not intensity information. Gradient orientation information does not depend on contrast and can describe the internal pattern structures of objects even in unclear IR images. We use the GMM to describe the topological structures of the internal patterns of vehicles. This approach can also eliminate the influences due to small differences between patterns. Evaluation tests with actual infrared video sequences have proved that the proposed algorithm provides stable vehicle detection.

Research on Deep Learning Automatic Vehicle Recognition Algorithm Based on RES-YOLO Model

“…In the 1960s, researchers began to conduct preliminary research on vehicle target detection [ 1 , 2 ]. It is a wide consensus that vehicle target detection is the basis of high-end technologies such as traffic big data platform construction, vehicle information management, and automatic driving [ 3 , 4 ].…”