Enhanced Vision-Based Speed Estimation By Roadside Surveillance Cameras

Cheng Wei Peng; Tai You Lin; Chen Chien Hsu; Sheng Chung Huang

doi:10.1109/GCCE59613.2023.10315516

Enhanced Vision-Based Speed Estimation By Roadside Surveillance Cameras

Cheng Wei Peng^*, Tai You Lin, Chen Chien Hsu, Sheng Chung Huang

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Vision-based applications have been gradually applied to city management demands in recent years. This study proposes an enhanced speed estimation method that uses video data from publicly accessible government surveillance cameras. By incorporating YOLO and DeepSORT, the system deduced each vehicle's position and estimated their speed accurately in a regression manner. According to the preliminary experimental result, the proposed method can produce data regarding vehicle counting and speed estimation; it showed that the proposed method could gain comparable content to the ground truth of the transportation web service platform.

Original language	English
Title of host publication	GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	889-890
Number of pages	2
ISBN (Electronic)	9798350340181
DOIs	https://doi.org/10.1109/GCCE59613.2023.10315516
Publication status	Published - 2023
Event	12th IEEE Global Conference on Consumer Electronics, GCCE 2023 - Nara, Japan Duration: 2023 Oct 10 → 2023 Oct 13

Publication series

Name	GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics

Conference

Conference	12th IEEE Global Conference on Consumer Electronics, GCCE 2023
Country/Territory	Japan
City	Nara
Period	2023/10/10 → 2023/10/13

Keywords

Advanced Traffic Management System
ITS
Object Detection
Object Tracking

ASJC Scopus subject areas

Artificial Intelligence
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Safety, Risk, Reliability and Quality
Instrumentation
Atomic and Molecular Physics, and Optics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/GCCE59613.2023.10315516

Cite this

Peng, C. W., Lin, T. Y., Hsu, C. C., & Huang, S. C. (2023). Enhanced Vision-Based Speed Estimation By Roadside Surveillance Cameras. In GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics (pp. 889-890). (GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE59613.2023.10315516

Enhanced Vision-Based Speed Estimation By Roadside Surveillance Cameras. / Peng, Cheng Wei; Lin, Tai You; Hsu, Chen Chien et al.
GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics. Institute of Electrical and Electronics Engineers Inc., 2023. p. 889-890 (GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Peng, CW, Lin, TY, Hsu, CC & Huang, SC 2023, Enhanced Vision-Based Speed Estimation By Roadside Surveillance Cameras. in GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics. GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics, Institute of Electrical and Electronics Engineers Inc., pp. 889-890, 12th IEEE Global Conference on Consumer Electronics, GCCE 2023, Nara, Japan, 2023/10/10. https://doi.org/10.1109/GCCE59613.2023.10315516

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abstract = "Vision-based applications have been gradually applied to city management demands in recent years. This study proposes an enhanced speed estimation method that uses video data from publicly accessible government surveillance cameras. By incorporating YOLO and DeepSORT, the system deduced each vehicle's position and estimated their speed accurately in a regression manner. According to the preliminary experimental result, the proposed method can produce data regarding vehicle counting and speed estimation; it showed that the proposed method could gain comparable content to the ground truth of the transportation web service platform.",

keywords = "Advanced Traffic Management System, ITS, Object Detection, Object Tracking",

author = "Peng, {Cheng Wei} and Lin, {Tai You} and Hsu, {Chen Chien} and Huang, {Sheng Chung}",

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series = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",

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Y1 - 2023

N2 - Vision-based applications have been gradually applied to city management demands in recent years. This study proposes an enhanced speed estimation method that uses video data from publicly accessible government surveillance cameras. By incorporating YOLO and DeepSORT, the system deduced each vehicle's position and estimated their speed accurately in a regression manner. According to the preliminary experimental result, the proposed method can produce data regarding vehicle counting and speed estimation; it showed that the proposed method could gain comparable content to the ground truth of the transportation web service platform.

AB - Vision-based applications have been gradually applied to city management demands in recent years. This study proposes an enhanced speed estimation method that uses video data from publicly accessible government surveillance cameras. By incorporating YOLO and DeepSORT, the system deduced each vehicle's position and estimated their speed accurately in a regression manner. According to the preliminary experimental result, the proposed method can produce data regarding vehicle counting and speed estimation; it showed that the proposed method could gain comparable content to the ground truth of the transportation web service platform.

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M3 - Conference contribution

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Enhanced Vision-Based Speed Estimation By Roadside Surveillance Cameras

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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