An Automatic System for Detecting the Vehicle Registration Plate from Video in Foggy and Rainy Environments using Restoration Technique

Kaur, Savneet; Dhillon, Kamaljit Kaur; Manvi, Manvi; Singh, Rajdeep

doi:10.5120/17123-7717

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…The Global Camera Sensor installed on a traffic control signal can pinpoint the motion in the area by identifying the specific number plates within the detection field area [46]. It can also record the present location together with the vehicle number to the local edge server [47]. A smart local camera sensor (LCS) is deployed at the vehicle's front side to record the activities of the driver.…”

Section: Autonomous Vehiclementioning

confidence: 99%

A Survey on Vehicular Edge Computing: Architecture, Applications, Technical Issues, and Future Directions

Raza

Wang

Ahmed

et al. 2019

Wireless Communications and Mobile Computing

161

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A new networking paradigm, Vehicular Edge Computing (VEC), has been introduced in recent years to the vehicular network to augment its computing capacity. The ultimate challenge to fulfill the requirements of both communication and computation is increasingly prominent, with the advent of ever-growing modern vehicular applications. With the breakthrough of VEC, service providers directly host services in close proximity to smart vehicles for reducing latency and improving quality of service (QoS). This paper illustrates the VEC architecture, coupled with the concept of the smart vehicle, its services, communication, and applications. Moreover, we categorized all the technical issues in the VEC architecture and reviewed all the relevant and latest solutions. We also shed some light and pinpoint future research challenges. This article not only enables naive readers to get a better understanding of this latest research field but also gives new directions in the field of VEC to the other researchers.

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Section: Autonomous Vehiclementioning

confidence: 99%

A Survey on Vehicular Edge Computing: Architecture, Applications, Technical Issues, and Future Directions

Raza

Wang

Ahmed

et al. 2019

Wireless Communications and Mobile Computing

161

View full text Add to dashboard Cite

show abstract

“…The LCS is equipped to generate these warning messages and, later broadcasting a specific number, informs the edge server about any interrupting activities involving the vehicle. This report involves activity evidence and vehicle identification [52][53][54][55][56].…”

Section: • Safety Applicationsmentioning

confidence: 99%

Exploring Computing Paradigms for Electric Vehicles: From Cloud to Edge Intelligence, Challenges and Future Directions

Chougule,

Chaudhari,

Ghorpade

et al. 2024

WEVJ

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Electric vehicles are widely adopted globally as a sustainable mode of transportation. With the increased availability of onboard computation and communication capabilities, vehicles are moving towards automated driving and intelligent transportation systems. The adaption of technologies such as IoT, edge intelligence, 5G, and blockchain in vehicle architecture has increased possibilities towards efficient and sustainable transportation systems. In this article, we present a comprehensive study and analysis of the edge computing paradigm, explaining elements of edge AI. Furthermore, we discussed the edge intelligence approach for deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network. It mentions the advantages of edge intelligence and its use cases in smart electric vehicles. It also discusses challenges and opportunities and provides in-depth analysis for optimizing computation for edge intelligence. Finally, it sheds some light on the research roadmap on AI for edge and AI on edge by dividing efforts into topology, content, service segments, model adaptation, framework design, and processor acceleration, all of which stand to gain advantages from AI technologies. Investigating the incorporation of important technologies, issues, opportunities, and Roadmap in this study will be a valuable resource for the community engaged in research on edge intelligence in electric vehicles.

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“…The Global Camera Sensor (GCS) deployed on traffic control signal can detect a motion on the scene for detecting the vehicle"s number plate within the detection field area and record the current location along with the vehicle number [21,30] to the local fog server. Flowchart 1 represents clearly represents this activity.…”

Section: Detecting Vehicle Number With the Help Of Gcsmentioning

confidence: 99%

A Fog-Based DSS Model for Driving Rule Violation Monitoring Framework on the Internet of Things

Roy¹,

Bose²,

Sarddar³

2015

IJAST

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An Automatic System for Detecting the Vehicle Registration Plate from Video in Foggy and Rainy Environments using Restoration Technique

Cited by 6 publications

References 3 publications

A Survey on Vehicular Edge Computing: Architecture, Applications, Technical Issues, and Future Directions

A Survey on Vehicular Edge Computing: Architecture, Applications, Technical Issues, and Future Directions

Exploring Computing Paradigms for Electric Vehicles: From Cloud to Edge Intelligence, Challenges and Future Directions

A Fog-Based DSS Model for Driving Rule Violation Monitoring Framework on the Internet of Things

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