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Intelligent Traffic Management System using Multi-Agent Reinforcement Learning 3

Intelligent Traffic Management System using Multi-Agent Rein...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Shaheen, Dalaali Paulraj, Getzi Jeba Leelipushpam Jebadurai, Immanuel Johnraja Kirubakaran, Stewart Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Cse Coimbatore India

ISBN: (纸本)9798331509675

This paper presents a decentralized multi-agent system for intelligent traffic management in urban environments, where each agent represents a traffic light controller at an intersection. The proposed system leverages a combination of Distributed W-Learning and Deep Q-Networks (DQN) to optimize traffic flow. Distributed W-Learning enables agents to prioritize decisions based on multiple performance policies, while DQN enhances their ability to handle complex state-action mappings through neural network approximations. By utilizing locally available traffic data, each agent adapts dynamically to diverse traffic conditions. The integration of these machine learning techniques ensures a fully decentralized and self-organizing approach, minimizing congestion, improving traffic efficiency, and addressing multiple objectives simultaneously. Simulation experiments are conducted using SUMO (Simulation of Urban MObility) to evaluate the system's performance in realistic traffic scenarios. This study highlights the potential of combining Distributed W-Learning and DQN in multi-agent reinforcement learning to revolutionize traffic management systems and achieve scalable, adaptive, and efficient urban traffic control. © 2025 IEEE.

关键词： Traffic congestion

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Exploration on computer vision Object Detection Algorithms under Artificial Intelligence 3

Exploration on Computer Vision Object Detection Algorithms u...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Qian, Wei School of Software Taiyuan University of Technology Shanxi Jinzhong China

ISBN: (纸本)9798350317022

In computer vision object detection, traditional detection methods often have poor performance when dealing with a large amount of background interference, target scale changes, and occlusion, and cannot capture all the complexity of the target, which will result in limited detection performance. In contrast, the application of computer vision object detection algorithms based on artificial intelligence in this field has become mainstream, driving the development of a series of efficient object detection methods. This article focuses on combining the convolutional neural network (CNN) algorithm to explore the relevant research on input image and candidate region selection, feature extraction and region classification, as well as optimization and evaluation indicators. Simulation experiments have concluded that compared to traditional visual detection, the improved accuracy index is improved by about 9.3%, while the recall performance index is improved by 20.5%. This study has improved the accuracy and efficiency of object detection, enabling computers to more reliably understand and interact with the real world, thus providing a solid foundation for automation and intelligent applications, with the potential to improve quality of life and promote technological innovation. © 2023 IEEE.

关键词： Artificial Intelligence computer vision Convolutional Neural Network Object Detection

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A Data Driven Approach for computer vision based Sign Language Model using Convolutional Neural Network 3

A Data Driven Approach for Computer Vision based Sign Langua...

引用

3rd international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Kiruba, R. Saranya, M. Radhika, V. Kiruthika, R. Mubashira, M.Fathima Sri Ramakrishna Engineering College Coimbatore India

ISBN: (纸本)9798350306989

Human beings communicate with each other with the help of speech. However, not everyone on our planet is endowed with the ability to talk and hear. People with such difficulties in speech and hearing, cannot communicate with each other by normal means. So, they use set of signs and hand gestures to communicate with each other. The set of such hand signs and gestures is known as sign language. Yet, they cannot communicate with normal people as most people out there do not know how to use sign language. The available methods are applied only on the level of alphabets, making them useless for live time usage. The primary goal of this research work is to convert sign language into speech on world level with the help of computer vision, tensorflow and natural language processing by extracting features from hand signs along the features from facial emotions as facial emotions also play vital role in the world level sign language. The hand signs and gestures along with the facial emotion features are converted into text with the help of an object detection model supported by tensorflow and computer vision. The artificial intelligence techniques - convolution neural network has been used for the training and predicting the speech input feed. The text output from the object detection model is then converted into speech with the help of pyttsx package, which is a renowned python package in the field of natural language processing. © 2023 IEEE.

关键词： Object detection

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ORB image feature extraction algorithm based on fuzzy control 3

ORB image feature extraction algorithm based on fuzzy contro...

引用

3rd international conference on Machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Li, Ruilong College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9781510681880

With the increasing demands for real-time accuracy in visual SLAM (Simultaneous Localization and Mapping), the critical front-end component of SLAM systems has become the extraction and matching of image features to estimate camera motion pose. Existing feature extraction methods are susceptible to local feature point density and even overlap due to uncertainties in lighting intensity and surrounding environments, leading to uneven distribution of overall feature points. This, in turn, affects the accuracy of feature matching, thereby impacting the performance of the SLAM process. To address these challenges, this paper proposes a novel ORB image feature extraction algorithm based on fuzzy control. Initially, a Gaussian pyramid of the image is constructed, and the Intensity Centroid is calculated to ensure scale and rotation invariance. Subsequently, candidate feature points are extracted using ORB at each pyramid level, then the candidate feature points are selectively filtered through fuzzy control based on the number of candidate feature points and the magnitude of their response values. Finally, a BRIEF descriptor is established for feature matching. The proposed Fuzzy-controlled ORB Feature Extraction (OFFC) algorithm was experimentally validated on open-source TUM and Mikolajczyk datasets. Results indicate that OFFC successfully avoids excessive density and overlap of extracted feature points, reduces mismatches in image feature points, and enhances the accuracy of feature matching. Notably, OFFC demonstrates adaptability and maintains robust performance in three challenging scenarios: image blurring, varying brightness, and dense similarity of image blocks, highlighting its significant practical value for visual SLAM. © 2024 SPIE.

关键词： Fuzzy control

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Smart ANPR: A Checkpoint-Based Toll and Parking Management System Using Hybrid OCR and Blockchain 3

Smart ANPR: A Checkpoint-Based Toll and Parking Management S...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Samarth, M.N. Gopisetti, Sampriti Chaitra, M. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Automated Number Plate Recognition is one of the most important applications of computer vision, employed in the enforcement of traffic law, vehicular management, security, and even at toll collection. This paper proposed an advanced system for ANPR that can move away from the traditional booth and establish a checkpoint. The strategically located cameras with checkpointing will automatically capture the plates of passing vehicles, permitting road tax and parking fees to be deducted without making contact. A hybrid approach that combines the capabilities of OpenCV, Tesseract OCR, and deep-learningbased OCR models helps tackle such challenges as stable images from live video feeds and highly accurate text recognition. To this end, since the system integrates high-resolution frame stabilization using OpenCV to ensure crystal clear captures, it further employs the use of YOLOv8 to boost the precision of real-time detection. The blockchain technology is further implemented to ensure that transactions are tamper proof, therefore ensuring secure and transparent billing systems. This makes the ANPR solution deployable on edge devices, which opens real-time processing capabilities for high-speed and high-volume environments, thereby offering a scalable and efficient approach for intelligent transportation and smart city applications. © 2025 IEEE.

关键词： Blockchain

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Research and implementation of fingerprint erasure technology for videos based on Google MediaPipe 3

Research and implementation of fingerprint erasure technolog...

引用

3rd international conference on computer vision and Data Mining, ICCVDM 2022

作者： Zhang, Tao Liu, Hongying Nanjing University of Science & Technology Zijin College Wenlan Rd. 89 Nanjing210033 China

ISBN: (纸本)9781510661363

Fingerprints are used in many fields as important biological data of people. As one of people's important personal privacy, fingerprints are also prone to leakage. Images and videos posted on social media are invitations to this problem. Research in the direction of protection of fingerprint privacy is nearly vacant. Therefore, this paper presents a technical implementation for locating and erasure human fingerprints for videos. This implementation adopted Google's open-source MediaPipe machine learning framework to identify the hand in the frame and obtain crucial landmark information. The fingerprint core range and finger state are deduced from landmark points' positional relationship and distance. This method can efficiently erase as many as 88% of frames with one hand presenting. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Palmprint recognition

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Crop Disease Detection using Hybrid Model of CNN and Capsule Networks 3

Crop Disease Detection using Hybrid Model of CNN and Capsule...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chayadevi, M.L. Shetty, Sameeksha Shetty, Shriddhi Bnm Institute of Technology Affiliated to Vtu Dept. of Cse Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Incorporation of DL techniques have greatly advanced in accuracy of prediction in various fields. In this research, our goal is to diagnose crop damage in fields by enabling farmers to identify the crop disease and take suitable actions. The proposed solution leverages hybrid model of CNN and CapsNets for image-based classification, accessible via a web interface, to facilitate this accurate crop disease prediction. After reviewing over 50 CNN articles, we identified that usage of Capsule Networks along with CNN enhances image classification acquiring better spatial relationships. © 2025 IEEE.

关键词： Productivity Accuracy Computational modeling Crops Predictive models Pest control Feature extraction Object recognition Diseases Image classification

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Fortifying the Power Eye: Counteracting Adversarial Attack in Power System Inspection 3

Fortifying the Power Eye: Counteracting Adversarial Attack i...

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3rd international conference on Power Systems and Electrical Technology, PSET 2024

作者： Wei, Zixiang Li, Yun Wang, Ruoyu Yuan, Zhikang University of Warwick Warwick Manufacturing Group Coventry United Kingdom Power Supply Company State Grid Hangzhou Hangzhou China Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States Tongji University College of Electronic and Information Engineering Shanghai China

ISBN: (纸本)9798350353204

This paper pioneers the exploration of resilience in power system vision models against adversarial attacks, which pose a significant threat to the reliability and security of critical infrastructure monitoring systems. The impacts of adversarial perturbations and patches, prevalent in smart grid applications, on the robustness of deep learning vision systems have been investigated. Through extensive experiments, we propose an innovative adaptive distillation-based defense strategy that enhances model robustness by leveraging a dual training approach. Our study not only demonstrates the vulnerability of conventional systems to these attacks but also provides a comprehensive analysis and effective mitigation techniques that can be adapted to various backbone networks, thereby ensuring more secure power system monitoring. © 2024 IEEE.

关键词： Adversarial machine learning

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Safeguarding the Homeless Using ViTransformer in Violence Detection 3

Safeguarding the Homeless Using ViTransformer in Violence De...

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3rd IEEE international conference on Intelligent Techniques in control, Optimization and Signal Processing, INCOS 2024

作者： Bhuvaneswari, R. Sai Lasya, M. Natarajan, B. Tamilselvi, M. Geetha, K. Velammal, M. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering Vit University Tamilnadu Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Karpagam College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350361186

In recent years, the issue of homelessness has gained significant attention as societies grapple with finding solutions to alleviate the plight of those without stable shelter. To address this pressing concern, this research utilizes advanced video analysis techniques, specifically vision Transformer, to detect the presence of individuals in homeless populations. By leveraging state-of-the-art technology, the research aims to not only identify individuals but also discern their actions and activities within the monitored environment. The methodology involves the initial detection of persons using Vi Transformer, an innovative deep learning model renowned for its accuracy in object recognition tasks. Once a person is successfully identified, this research further scrutinizes their activities, with a particular focus on detecting instances of conflict or fighting. This step holds immense potential or the safety and well-being of homeless individuals, as it enables the swift notification of nearby authorities or support services in cases of altercation. Crucially, this research delves into the performance metrics of both person detection and activity detection experiments, providing valuable insights into the efficacy of the Vi Transformer model in real-world applications. By assessing the system's accuracy, speed, and reliability, this study contributes to the development of proactive strategies for assisting homeless populations, emphasizing the importance of technology in addressing social challenges. © 2024 IEEE.

关键词： Object recognition

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Resilient 3D Object Recognition using GR-Net in Sparse Point Clouds 3

Resilient 3D Object Recognition using GR-Net in Sparse Point...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ghadekar, Premanand Dhame, Pratik Dixit, Soham Patil, Arpit Sanjekar, Rushikesh Shinde, Siddhesh Vishwakarma Institute of Technology Department of Information Technology Maharashtra Pune India

ISBN: (纸本)9798331523893

Recognizing three-dimensional (3 Dimensonal) objects is crucial for several uses for computer vision like service robots, self-driving cars and surveillance drones to navigate effectively in complex environments. However, existing classification techniques struggle with challenges such as varying resolutions, noisy data, and diverse object poses. Previous studies have highlighted the limitations of point cloud-based methods in handling sparsity, rotation, and positional variance. In this study, we concurrently address these difficulties with a unique technique to 3D object categorization. Our method leverages the graph structure of point clouds and employs a to develop a strong latent representation of 3D objects using a neural network. This representation achieves invariance to rotation, positional shift, and scaling while remaining resilient to point sparsity. Our technique outperforms existing approaches, as evidenced by experimental findings on the ModelNet40 dataset, which show a 45.0% and 38.1% gain in classification accuracy when employing sparse point clouds, respectively, over existing models. © 2025 IEEE.

关键词： Object detection

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