Image Processing Based Smart Traffic Control System for Smart City

Bhardwaj, Vedansh; Rasamsetti, Yaswanth; Valsan, Vipina

doi:10.1109/icccnt51525.2021.9579787

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…This scheme, with an emphasis on time efficiency, is mainly focused on real-time response and addresses the critical requirements for E-health big data processing in healthcare systems. D. Intelligent traffic management system based on image processing for Smart City [4] The paper authored by Vedansh Bhardwaj, Yashwanth Rasamsetti, and Vipina Valsan, titled "Image Processing Based Smart Traffic Control System for Smart City," published on July 2021, introduces an innovative traffic control system. This system leverages image processing techniques to dynamically analyze traffic conditions in real-time, enabling adaptive responses to varying traffic scenarios.…”

Section: Literature Survey Overviewmentioning

confidence: 99%

“…. A New Image Encryption Algorithm for Gray and Color Medical Images[6] Contribution by SARA T. KAMAL, KHALID M. HOSNY, TAHA M. ELGINDY, MOHAMED M. DARWISH AND MOSTAFA M. FOUDA[4] titled "A New Encryption Algorithm for Gray and Color Medical Images", published on March 2, 2021, presents a new image encryption technique adapted for medical images. The method uses an image segmentation technique based on blocks that are encoded using a zigzag pattern, rotation, and random permutation.…”

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confidence: 99%

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Advanced Surveillance System with Encrypted Alerts and Privacy Enhancement

Kumar H S,

Nab,

M R

et al. 2024

IJSREM

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The proposed project "Advanced Surveillance System with Encrypted Alerts and Privacy Enhancement" addresses the critical need to increase public safety through a sophisticated security surveillance system. This multi-faceted system integrates core functions including real-time object detection, encrypted image capture, secure email alerts and comprehensive security measures. Different algorithms are used for face detection, which allows the system to continuously evolve and improve its object detection capabilities and adapt to real-world scenarios. The primary goal is to identify specific objects in public spaces and thus contribute to safer communities. The project further prioritizes the protection of captured and transmitted data, implementing strict security measures to protect against attempts to hack email accounts and decrypt images. This project represents a significant leap forward in security and surveillance, offering a versatile and adaptable solution designed to effectively address evolving public safety challenges.. Keywords — Encryption, Decryption, Cryptography, Object Detection, RNS, Machine learning algorithms.

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Section: Literature Survey Overviewmentioning

confidence: 99%

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confidence: 99%

Advanced Surveillance System with Encrypted Alerts and Privacy Enhancement

Kumar H S,

Nab,

M R

et al. 2024

IJSREM

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“…The intensity and density of vehicles may be increasing in one lane, while decreasing in another. Urban planners can optimize traffic light signal timing, enforce dynamic re-routing strategies that seek to mitigate congestion employing alternatives to vehicular traffic, and study this kind information in an effort to makes decisions about transportation such as buses [14], [15]. In addition to the reduction of traffic, and environmental sustainability is becoming a major concern for smart cities.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Integrating IoT and WSN with Predictive Modeling and Optimization for Enhancing Efficiency and Sustainability in Smart Cities

S. Srinivasan

2024

jes

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The development of smart city solutions is necessary for traffic management and environmental sustainability to meet the challenges of global urbanization. Based on this idea, the paper presents a new way to amalgamate IoT and Wireless Sensor Networks with predictive modeling and optimization methods for use in smart city management. To this end, we have installed traffic sensors (infrared, ultrasonic) and environmental sensors (air quality, humidity, gas) at strategic points throughout the city where they can collect real-time data on traffic and pollution. Then, this data is used together with machine learning models such as ANN (Artificial Neural Network), DT or Decision Trees, KNN (K-Nearest Neighbors), and RF or Random Forests. In practical terms, it is through iterative training processes that our models have achieved ever greater accuracy over time. Now they can actually learn and adapt to changing urban dynamics. The holistic solution of this approach pertains to informed decision-making in smart city infrastructure management. Urban stakeholders can make data-driven decisions by leveraging advanced sensor technologies and machine learning algorithms to deal with traffic congestion, reduce pollution and enhance the quality of life for smart cities. Urban planners, policymakers and technologists working on smart city solutions to improve urban mobility and environmental quality will all be interested in the outcomes of this study.

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