A Calculation Method for Vehicle Movement Reconstruction from Videos
| Autor: | Weiwei Heng, Feng Hao, Feng Chen, Weiguo Shi, Young-Ji Byon, Shaoyou Pan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Economics and Econometrics
Article Subject Computer science Strategy and Management 0211 other engineering and technologies Phase (waves) sync 02 engineering and technology 021105 building & construction 0502 economics and business Calibration Computer vision HE1-9990 050210 logistics & transportation Data collection TA1001-1280 Movement (music) business.industry Mechanical Engineering Track (disk drive) 05 social sciences Perspective (graphical) Computer Science Applications Transportation engineering Automotive Engineering Artificial intelligence Direct linear transformation business Transportation and communications |
| Zdroj: | Journal of Advanced Transportation, Vol 2020 (2020) |
| ISSN: | 0197-6729 |
| DOI: | 10.1155/2020/8896826 |
| Popis: | This paper proposes a new enhanced method based on one-dimensional direct linear transformation for estimating vehicle movement states in video sequences. The proposed method utilizes a contoured structure of target vehicles, and the data collection procedure is found to be relatively stable and effective, providing a better applicability. The movements of vehicles in the video are captured by active calibration regions while the spatial consistency between the vehicle’s driving track and the calibration information are in sync. The vehicle movement states in the verification phase are estimated using the proposed method first, and then the estimated states are compared with the actual movement states recorded in the experimental test. The results show that, in the case of camera perspective of 90 degrees, in all driving states of low speed, high speed, or deceleration, the error between estimated speed and recorded speed is less than 1.5%, the error of accelerations is less than 7%, and the error of distances is less than 2%; similarly, in the case of camera perspective of 30 degrees, the errors of speeds, distances, and accelerations are less than 4%, 5%, and 10%, respectively. It is found that the proposed method is superior to other existing methods. |
| Databáze: | OpenAIRE |
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