2020
DOI: 10.1007/978-3-030-61705-9_49
|View full text |Cite
|
Sign up to set email alerts
|

Identifying and Counting Vehicles in Multiple Lanes by Using a Low-Cost Vehicle-Mounted Sensor for Intelligent Traffic Management Systems

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
6
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
2
1
1

Relationship

2
2

Authors

Journals

citations
Cited by 4 publications
(6 citation statements)
references
References 18 publications
0
6
0
Order By: Relevance
“…In our recent studies [9], [10], data were collected with a vehicle equipped with a monocular camera with a built-in GPS receiver. The purposes of those studies were to use the ego-vehicle as a mobile sensor, estimating traffic data for surrounding vehicles, in order to share them with ITMS.…”
Section: Methodsmentioning
confidence: 99%
“…In our recent studies [9], [10], data were collected with a vehicle equipped with a monocular camera with a built-in GPS receiver. The purposes of those studies were to use the ego-vehicle as a mobile sensor, estimating traffic data for surrounding vehicles, in order to share them with ITMS.…”
Section: Methodsmentioning
confidence: 99%
“…To detect lanes, as we presented in [8] and [9], we used canny edge detection [27] and the progressive probabilistic Hough transform [28][29].…”
Section: Ii) Approach 2: Geometric Computationmentioning
confidence: 99%
“…Therefore, in this paper, we go beyond the lane-level target-vehicle localization presented in [9] and find the latitude and longitude of a target vehicle in a GPS coordinate system dynamically while both the ego vehicle and the target vehicle are moving in a metropolitan area. Although some research has been carried out on utilizing an ego vehicle as a mobile sensor to estimate traffic data of the target vehicle, there is still very little scientific understanding of estimating the geolocation of HDVs based on ego-vehicle self-localization, image-based estimated distance to the target vehicle, and the relative angle between them by using a monocular camera with a built-in GPS receiver mounted on a mobile ego vehicle.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…The proposed technique outperformed the conventional CNN in-vehicle image analysis, according to experimental observations. Furthermore, Namazi et al [15] invented different methods to enhance a traffic control system when there is a mix of modern vehicles and human-driven vehicles with varying degrees of autonomy. The results indicated that the algorithms can correctly determine the type of detected vehicle in the widely researched scenarios 95.21% of the time.…”
Section: Literature Reviewmentioning
confidence: 99%