In the past, most of the research on car-following models in China focused on physical statistics, with relatively little collection and analysis of car-following data. Therefore, it is essential to establish a car-following model suitable for analyzing the car-following characteristics of Chinese drivers based on real driving data. This paper utilizes a vehicle-mounted monocular camera to capture traffic flow images on the highway and uses the YOLOv5 algorithm to detect vehicles. Through the homography transformation between the image plane in the front view and the spatial plane in the top view, the pixel coordinates are converted to 3D spatial coordinates. Then, the ranging model is established, and its effectiveness is verified using the length of the lane dividing lines. The factor analysis is used to determine the main influencing factors of car-following behavior. The appropriate linear car-following models are selected and its parameters are calibrated using multiple linear regression methods. This paper realizes the collection and subsequent processing of car-following behavior data under natural driving conditions, and transforms generalized car-following models into specific research theories for application in practical situations. This method of collecting car-following driving data has strong practicality and can provide effective data support for studying car-following models that accurately reflect the operation conditions of road traffic.