Autonomous Real-Time Multiple Vehicles Detection and Tracking System
Several traffic studies require vehicle counting in peak hours and during the day with detailed classification and tracking, which exhaust human time and effort, especially at intersections. Manual efforts mainly collect the necessary traffic demand data live in the field or from video records with an extended data manipulation process. Alternative solutions are computer-based systems that efficiently perform human tasks with less time and effort, and these systems vary in their function and performance. This paper proposed a full computer-based system that detects, tracks, and computes related statistics in real-time and maximum utilization of available resources, such as public road surveillance cameras. This work's main contribution is the effectiveness of gathering different computer vision algorithms to achieve high accuracy performance during the real-time streaming of road cameras. The experiments confirm the system performance by achieving, on average, 93.2% as a success rate. The novel addition in this work is that detections, point extractions, matching, tracking, and classification were implemented in a single system that guarantees real-time execution, high accuracy output, and utilizes the available infrastructure. The system overcomes the varying in the light through day and night, and between cloudy and shining weather. Also, it recovers hidden vehicles and the changing in view for each vehicle through its movement. The proposed approach efficiently and partially gathers some mechanisms mentioned above in one system.
- Record URL:
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Supplemental Notes:
- This paper was sponsored by TRB committee ACP15 Standing Committee on Intelligent Transportation Systems.
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Corporate Authors:
Transportation Research Board
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Authors:
- Lebdeh, Enas Abu
- Awad, Bashar M
- Alomari, Ahmad H
- Hawamdeh, Mohammed S
- Karasneh, Mohammad A
- Al-Qudah, Sara M
- Ajjawi, Yara B
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Conference:
- Transportation Research Board 100th Annual Meeting
- Location: Washington DC, United States
- Date: 2021-1-5 to 2021-1-29
- Date: 2021
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Photos; References;
- Pagination: 17p
Subject/Index Terms
- TRT Terms: Algorithms; Automatic vehicle detection and identification systems; Cameras; Machine vision; Real time data processing; Traffic counting
- Subject Areas: Data and Information Technology; Highways;
Filing Info
- Accession Number: 01763930
- Record Type: Publication
- Report/Paper Numbers: TRBAM-21-00617
- Files: TRIS, TRB, ATRI
- Created Date: Feb 4 2021 10:57AM