摘要
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.
| 原文 | 英語 |
|---|---|
| 主出版物標題 | GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics |
| 發行者 | Institute of Electrical and Electronics Engineers Inc. |
| 頁面 | 889-890 |
| 頁數 | 2 |
| ISBN(電子) | 9798350340181 |
| DOIs | |
| 出版狀態 | 已發佈 - 2023 |
| 事件 | 12th IEEE Global Conference on Consumer Electronics, GCCE 2023 - Nara, 日本 持續時間: 2023 10月 10 → 2023 10月 13 |
出版系列
| 名字 | GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics |
|---|
會議
| 會議 | 12th IEEE Global Conference on Consumer Electronics, GCCE 2023 |
|---|---|
| 國家/地區 | 日本 |
| 城市 | Nara |
| 期間 | 2023/10/10 → 2023/10/13 |
UN SDG
此研究成果有助於以下永續發展目標
-
SDG 11 永續城鄉
ASJC Scopus subject areas
- 人工智慧
- 能源工程與電力技術
- 電氣與電子工程
- 安全、風險、可靠性和品質
- 儀器
- 原子與分子物理與光學
指紋
深入研究「Enhanced Vision-Based Speed Estimation By Roadside Surveillance Cameras」主題。共同形成了獨特的指紋。引用此
- APA
- Standard
- Harvard
- Vancouver
- Author
- BIBTEX
- RIS