An Improved Speed Estimation Using Deep Homography Transformation Regression Network on Monocular Videos
Issued Date
2023-01-01
Resource Type
eISSN
21693536
Scopus ID
2-s2.0-85147287430
Journal Title
IEEE Access
Volume
11
Start Page
5955
End Page
5965
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Access Vol.11 (2023) , 5955-5965
Suggested Citation
Yohannes E., Lin C.Y., Shih T.K., Thaipisutikul T., Enkhbat A., Utaminingrum F. An Improved Speed Estimation Using Deep Homography Transformation Regression Network on Monocular Videos. IEEE Access Vol.11 (2023) , 5955-5965. 5965. doi:10.1109/ACCESS.2023.3236512 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/81800
Title
An Improved Speed Estimation Using Deep Homography Transformation Regression Network on Monocular Videos
Author's Affiliation
Other Contributor(s)
Abstract
Vehicle speed estimation is one of the most critical issues in intelligent transportation system (ITS) research, while defining distance and identifying direction have become an inseparable part of vehicle speed estimation. Despite the success of traditional and deep learning approaches in estimating vehicle speed, the high cost of deploying hardware devices to get all related sensor data, such as infrared/ultrasonic devices, Global Positioning Systems (GPS), Light Detection and Ranging (LiDAR systems), and magnetic devices, has become the key barrier to improvement in previous studies. In this paper, our proposed model consists of two main components: 1) a vehicle detection and tracking component - this module is designed for creating reliable detection and tracking every specific object without doing calibration; 2) homography transformation regression network - this module has a function to solve occlusion issues and estimate vehicle speed accurately and efficiently. Experimental results on two datasets show that the proposed method outperforms the state-of-the-art methods by reducing the mean square error (MSE) metric from 14.02 to 6.56 based on deep learning approaches. We have announced our test code and model on GitHub with https://github.com/ervinyo/Speed-Estimation-Using-Homography-Transformation-and-Regression-Network.