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OBJECTIVE: To address the issue of calculating vehicle speed from original videos due to unclear reference objects and feature points, this study employs calibration cloth for on-site reexamination and combines image fusion technology for video speed identification. METHODS: In the process of constructing an experimental model for vehicle speed measurement using the calibration cloth method, image fusion was performed between the original video footage and the calibration cloth video. The calibration cloth used was a black-and-white checkerboard pattern consisting of 25 rows and 9 columns, with each grid measuring 20 cm × 20 cm. Three vehicle types-sedan, MPV, and SUV-were selected for the experiment. Actual vehicle speeds were calculated across six speed intervals (40 km/h, 50 km/h, 60 km/h, 70 km/h, 80 km/h, and 90 km/h). The speed error rates were analyzed and compared with those obtained from the traditional wheelbase method and the monocular vision velocimetry method based on YOLO+DeepSORT + DepthAnything, thereby verifying the feasibility, accuracy, and advantages of the calibration cloth method. The proposed method was further validated through analysis of real-world accident cases. RESULTS: The proposed method achieved satisfactory image fusion performance, demonstrating good feasibility, stability, and accuracy. Its effectiveness was particularly pronounced within the 40-70 km/h speed range. Compared with the traditional wheelbase method, the calibration cloth method yielded smaller speed estimation errors; compared with the monocular vision velocimetry method, it exhibited superior stability. Furthermore, the method is applicable to speed measurement for various vehicle types and can effectively reflect vehicle motion states. CONCLUSION: This paper proposes a novel vehicle speed measurement method-the calibration cloth method-which is characterized by high accuracy, strong stability, wide applicability, and operational simplicity. The promotion and application of this method will help expand the applicable conditions of video-based speed forensic analysis and improve measurement quality.
Zhang et al. (Thu,) studied this question.