Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems [Literature Review]

Gao, Jingqin; Ozbay, Kaan; Xu, Chuan; Zhang, Daniel; Zuo, Fan; Yang, Liu; Hammami, Omar

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Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems [Literature Review]

2023-05-01
By Gao, Jingqin ; Ozbay, Kaan ; Xu, Chuan ; ...

File Language:

English

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Creators:

Gao, Jingqin ; Ozbay, Kaan ; Xu, Chuan ; Zhang, Daniel ; Zuo, Fan ; Yang, Liu ; Hammami, Omar
Corporate Creators:

New York University. Tandon School of Engineering
Corporate Contributors:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:

Autonomous Vehicles Cameras Computer Vision Connected Vehicles Cost Effectiveness Data Collection Intelligent Transportation Systems Literature Reviews Pedestrian Detectors Probe Vehicles Sensors Traffic Surveillance
Resource Type:

Tech Report
Geographical Coverage:

New York;United States
Edition:

Literature Review
Corporate Publisher:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
Abstract:

Computer vision is reshaping the transportation industry and bringing its unique capabilities to the table to enable next generation smart transportation systems in many different ways. The state-of-the-art of IoT strategies and computer vision techniques is well-studied in the literature, some has already been tested and used for certain use cases, but this technology has not widely applied in the day-to-day operation to existing transportation infrastructures yet. A comprehensive review of both state-of-art and state-of-practices as well as gaps in terms of use cases and applications is needed.
Format:

PDF
Funding:

69A3551747119
Collection(s):

University Transportation Centers Autonomous Vehicles
Main Document Checksum:

urn:sha-512:93d6985fcc0f21483070b2b71a19977c3da8faa630a63f16087a6392adca09249c8dcef24d742f6f0c17fc55553537d13b2f2a55082b6e6a032781d0c1cf1761
Download URL:

https://rosap.ntl.bts.gov/view/dot/72520/dot_72520_DS1.pdf
File Type:

[PDF - 861.11 KB ]

File Language:

English

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