Speed estimation using deep learning with optical flow

Nobuhiko Mukai; Naoki Nishimura; Youngha Chang

doi:10.1117/12.3017765

2 May 2024 Speed estimation using deep learning with optical flow

Nobuhiko Mukai, Naoki Nishimura, Youngha Chang

Proceedings Volume 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024; 1316406 (2024) https://doi.org/10.1117/12.3017765
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2024, 2024, Langkawi, Malaysia

Abstract

For automatic driving, it is necessary to perform a lot of simulations that consider various kinds of situations including hazardous events. In the simulations, driving scenarios are needed and should be generated from real driving records. On the other hand, most vehicles have dashcams, and the video taken by the camera is used as evidence in case of car accidents or near-miss reports. In addition, Artificial intelligence (AI), which is achieved by using deep learning methods, is widely used to recognize many kinds of unknown objects. This paper reports a method and the result of deep learning-based estimation of automobile speed. We employ 3D CNN as the basic learning model and four-fold cross-validation as the accurate measure. First, 16-frame original images obtained by a dashcam, whose resolution is reduced for faster learning, are used for learning and validation. Four-fold cross-validation and estimation results are reported for the speed range from 70 [km/h] to 100 [km/h] in every 5 [km/h]. Then, the images with optical flow generated by two types of methods, which are the Lucas-Kanade and Gunner-Farnebäck methods, are utilized, and the estimation errors are reported for the speed range from 70 [km/h] to 100 [km/h] in every 1 [km/h]. We have confirmed that optical flow is useful for estimating automobile speed from dashcam video.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nobuhiko Mukai, Naoki Nishimura, and Youngha Chang "Speed estimation using deep learning with optical flow", Proc. SPIE 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024, 1316406 (2 May 2024); https://doi.org/10.1117/12.3017765

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available