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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VII"

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Real Time Violence Detection System Using YOLOv7 and Deep Learning Techniques

Real Time Violence Detection System Using YOLOv7 and Deep Le...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Sudha Senthilkumar Gracy Agarwal Arshia Shirish Shruti Kolte School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In recent times, violence in public spaces such as schools, hospitals, and other community environments has increased a lot. Due to this alarming rise in violence CCTV cameras have been deployed everywhere to enhance safety and security. However, these systems depend on manual monitoring which is prone to human errors and cause delays in the identification of violent events and action which can lead to significant damage. To overcome these limitations, this study has proposed a violence detection and alarm system based on deep learning algorithms. The hybridization of YOLOv7 allows rapid recognition of possible threats by identifying human presence and movement, and the lightweight classifier provides a high accurate classification of the identified tasks while reducing the processing computational resources of the system. With this dual-model-based framework, it is guaranteed that both the speed and accuracy can be achieved, and that the system can be implemented on edge devices such as mobile devices. The function of the system is to identify violence while it is happening and to issue alerts to security or law enforcement authorities committed to rapid response. The system was tested in real-time scenarios and achieved 91% accuracy in detecting violent actions. The system incorporates a Telegram bot to send real-time alerts to the relevant authorities. By leveraging advanced deep learning techniques, this study seeks to close the gap between existing surveillance methods and the need for a more automated and accurate violence detection system.

关键词： Deep learning Performance evaluation Visualization Accuracy Surveillance image edge detection Streaming media Real-time systems Safety Usability

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Comprehensive Review of Cybersecurity Vulnerabilities and Safety Concerns in Autonomous Vehicles

Comprehensive Review of Cybersecurity Vulnerabilities and Sa...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE International conference on

作者： Uppalapati Sudheer Kumar Sukanta Halder Basant Kumar Sethi Department of Electrical Engineering Sardar Vallabhbhai National Institute of Technology Surat Surat India Department of Electrical Engineering Indian Institute of Technology(ISM) dhanbad Dhanbad India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

Autonomous Vehicles (AVs) are among the leading technologies enhancing our world with their advanced autonomous driving capabilities and functional awareness. These AVs rely on sophisticated technologies such as weight sensors, speed sensors, tire pressure sensors, 3D imaging cameras, satellite-based positioning systems, and advanced radars like light detection and ranging (LiDAR) and 3D radar. AVs utilize these components, along with highly complex control algorithms as their central processing units, to perform autonomous driving, making them vulnerable to cybersecurity attacks. These security attacks raise significant safety concerns for AVs. This article provides a comprehensive review of various cybersecurity threats associated with AVs and presents potential solutions. Additionally, it examines the vulnerabilities of the sophisticated equipment used in AVs. This article aims to enhance the understanding of the safety and security concerns associated with AVs and offers insights into the importance of cybersecurity in this evolving technological field. Furthermore, this article addresses the current state of cybersecurity measures in AVs, evaluating the effectiveness of existing protocols and identifying areas that require improvement. It also explores emerging technologies and strategies that can enhance the security of AVs, such as blockchain for secure data transactions, machine learning for threat detection, and robust encryption methods.

关键词： image sensors Radar imaging Threat assessment Sensor systems Safety Sensors Encryption Security Computer security Autonomous vehicles

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Integrated Recognition Approach and Fining System Towards Detection and Tracking of Traffic Violating Multiple Vehicles using Pairing Net and Light Weight Deep Sort Fast YOLO Rec Architectures

Integrated Recognition Approach and Fining System Towards De...

引用

International conference on ent Computing and Control systems (ICICCS)

作者： R. Mugesh R. Manoj R. Kaviprasth S. Gokilavani Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Innovations in intelligent transport management systems have enhanced vehicle anomaly detection through traffic monitoring sensors and computer vision. Current approaches, however, lack effectiveness in multi-vehicle detection and tracking amidst changing traffic, especially for overspeeding, overloading, helmet violations, frequent lane changes, and imitation or unrecognized license plates. This research study proposes an end-to-end recognition method and self-driving fining system based on PairingNet + Lightweight Deep SORT Fast YOLO-Rec Architecture in order to provide improved detection efficiency and speed. Real-time sensor data from benchmark datasets or live video streams is processed into the form of frames. The multi-scale features are extracted by Fast YOLO-Rec utilizing a CNN backbone with Cross-Stage Partial (CSP) Connection Blocks, Residual Blocks, and Dark Blocks. Path Aggregation Network (PANet) maintains precise detection for objects of multiple sizes. For tracking, an anchor box prediction technique produces bounding boxes, and PairingNet, which is a graph convolution network-based method, pairs vehicles between frames based on contour and texture similarity. Deep SORT algorithm classifies offenses like counterfeit license plates, helmet misuse, and overloaded vehicles. Alerts are triggered to authorities and vehicle owners when infractions are detected. Experimental results show that the suggested model performs better than traditional methods in detection accuracy and processing efficiency and is a stable solution for traffic law enforcement and road safety.

关键词： Head image recognition Convolution Computational modeling Neural networks Computer architecture Feature extraction Safety Classification algorithms License plate recognition

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Intrusion Detection from Surveillance Video Using Frame Based image processing Techniques 1st

Intrusion Detection from Surveillance Video Using Frame Base...

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Mohamed Yaseen, M.S. Radeesh, P. Chitra, P. Ramesh, Kishore SRM Institute of Science and Technology Vadapalani Tamil Nadu Chennai India

ISBN: (纸本)9783031823824

In India, the number of surveillance cameras has increased rapidly, making it challenging for law enforcement to go through the vast amount of recorded footage. We suggest a technique for quickly reviewing large amounts of video feed in order to address this issue. By utilizing sophisticated algorithms such as facial recognition, lightweight YuNet model face detection, and motion detection through background subtraction, our system accelerates the analysis of videos from various camera systems. Our model is a great tool for any agency looking to improve its operational efficiency when handling large amounts of monitoring data because it can quickly identify important events and produce concise summaries with timestamps. This piece of research contributes towards development within this field since such new methods are combined into one single solution thereby advancing surveillance technology while at the same time providing concrete benefits for everyday challenges faced by law enforcement agencies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Intrusion detection

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image Extraction for Linear Segment in 3D Space 3rd

Image Extraction for Linear Segment in 3D Space

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3rd International conference on Signal and Data processing, ICSDP 2023

作者： Mamatha, Elliriki Amardeepak, M. Sharma, Rohit Krishna Anand, S. Department of Mathematics GITAM University Bengaluru India Department of Mechanical Engineering CIT-NC Bengaluru India Department of Engineering Garden City University Bangaluru India Department of AI & DS Shridevi Institute of Engineering and Technology Tumkur India

ISBN: (纸本)9789819795772

In computer graphics systems, cropping applications appear to be quite simple from a human perspective as human eyes can easily visualize whether a portion of the picture is inside or outside. However, from a computational perspective, the problem increases multifold as a simple process involves a large number of comparisons and computations. With the added importance given to floating point numbers, the complexity shoots up exponentially. With this viewpoint in perspective, an analytical model has been designed. This model incorporates algorithms using integral calculations in order to find intermediate intersection points. This in turn would improve cropping objects’ speed in three-dimensional spaces. The performance of this algorithm is expected to be robust and can handle computations at a much higher speed for any size of rectangular parallelepiped cropping windows. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Three dimensional computer graphics

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Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm for Social Media Content Classification and Community Detection

Stereoscopic Gated Graph Scalable Quantum Capsule Convolutio...

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Machine Learning and Autonomous systems (ICMLAS), International conference on

作者： A. Elumalai S. Rukmani Devi D. Rosy Salomi Victoria A. Jagadeeswaran Aparajita Mukherjee K. Vinoth Department of Mathematics R.M.K. Engineering College Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Information Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Annapoorana Engineering College Salem Tamil Nadu India Department of Computer Science and Engineering Institute of Engineering and Management(IEM) University of Engineering and Management Kolkata West Bengal Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Social Media Content Classification and Community Detection (SMCCCD) classify content and identify communities through deep learning, and NLP. Traditional models are weak in scalability and interpretability. To solve all these problems, this paper introduces a Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks with Artificial Hummingbird Algorithm (SGGSQCCN2Nets+AHA) for social content classification and community detection. Data inputs are accessed from the Twitter dataset and are initially preprocessed by the Natural Language (NL) method. After preprocessing, content classification and community detection are done using the Stereoscopic Gated Graph Scalable Quantum Capsule Convolutional Neural Networks (SGGSQCCN2Nets) and optimization is then performed with the Artificial Hummingbird Algorithm (AHA). The performance of the new SGGSQCCN2Nets+AHA model is tested on a Twitter dataset with a high accuracy rate of 99.9% and specificity of 99.8%. The proposed model is developed by utilizing Python programming language. The outcome of the proposed SGGSQCCN2Nets+AHA model enhances the performance of content classification and improves user community detection accuracy. It is effective in addressing scalability issues, obtaining complex features, and offering more interpretability. The model is also resilient against noisy data dynamically adapts to evolving social media trends and outperforms traditional deep learning techniques in real-time analysis.

关键词： Adaptation models Accuracy Social networking (online) Stereo image processing Scalability Heuristic algorithms Blogs Logic gates Classification algorithms Convolutional neural networks

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Evaluation on the Effectiveness of SVM-Based image processing algorithms for the Identification and Classification of Lung Cancer 15th

Evaluation on the Effectiveness of SVM-Based Image Processin...

引用

15th International conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bhaskar, Nuthanakanti Swaroopa Rani, B. Srinivasula Reddy, A. Asha Shiny, X.S. Gayathri, B. Venkateswara Reddy, R. CMR Technical Campus Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9783031810794

Globally, lung cancer is a prevalent reason for death for individuals. The most common form of lung cancer has a high death rate. If lung cancer is identify and care for in its early on stages, screening can assist identify the disease at an earlier stage. Therefore, a patient's chances of survival are increased by early identification of lung cancer. Lung computed tomography (CT) scans might represent a more reliable method of identifying cancer than MRIs and X-rays. DICOM (Digital Imaging and Communications in Medicine) is used to scan the lungs and obtain images. We improve and smooth photos using a variety of preprocessing approaches. Edge detection and thresholding (ROI) are used in this work to section the lung tumor's region of interest. Lastly, we use Support Vector Machine to compute a number of geometrical aspects of the retrieved ROI and categorize the results into both benign and malignant degrees of severity (SVM). Data is classified using supervised machine learning techniques called SVM classifiers. One of their advantages is their ability to handle small amounts of high dimensional data. Every characteristic is used as an input to evaluate the SVM's efficiency. As such, lung cancer in CT scans is diagnosed using a method that uses methods for processing images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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image processing benchmark study

Image processing benchmark study

引用

conference on Machine Vision systems for Inspection and Metrology vii

作者： Miller, JWV Eddy, C Waltz, FM Hack, R Wood, J Stokes, D Univ Michigan Dept Elect & Comp Engn Dearborn MI 48128 USA

ISBN: (纸本)0819429821

This paper describes the benchmarking of image processing algorithms using high-performance workstations and personal desktop computers. For the various platforms evaluated which included machines from Sun, SGI, Apple, and Gateway, compiler options were varied to obtain the fastest execution times. algorithms evaluated included typical image processing operations such as derivatives, logical operations, morphology, subtraction, median filter, and the new SKIPSM approach. Data were collected using the different platforms and are presented here in tabular form. The results indicate that the latest generation of personal computers have processing capabilities that are similar to UNIX-based work stations.

关键词： benchmarking compiler optimization image processing image processing algorithms

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Efficient Detection of Ellipses from an image by a Guided Modified RANSAC

Efficient Detection of Ellipses from an Image by a Guided Mo...

引用

conference on image processing - algorithms and systems vii

作者： Xie, Yingdi Ohya, Jun Waseda Univ Grad Sch GITS Tokyo Japan

ISBN: (纸本)9780819474957

In this paper, we propose a novel ellipse detection method which is based on a modified RANSAC, with automatic sampling guidance from the edge orientation difference curve. Hough Transform family is one of the most popular and methods for shape detection, but the Standard Hough Transform loses its computation efficiency if the dimension of the parameter space gets high. Randomized Hough Transform, an improved version of Standard Hough Transform has difficulty in detecting shapes from complicated, cluttered scenes because of its random sampling process. As a pre-process for random selection of five pixels to be used to build the ellipse's equation, we propose a two-step algorithm: (1) region segmentation and contour detection by mean shift algorithm (2) contour splitting based on the edge orientation difference curve obtained from the contour of each region. In each contour segment obtained by step (2), 5 pixels are randomly selected and the modified RANSAC is applied to the 5 pixels so that an accurate ellipse model is obtained. Experimental result show that the proposed method can achieve high accuracies and low computation cost in detecting multiple ellipses from an image.

关键词： image segmentation Hough transforms Detection and tracking algorithms Data modeling image processing algorithms and systems Edge detection image processing Computer vision technology Machine vision Pattern recognition

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image Restoration Regularized by a Fourth Order PDE

Image Restoration Regularized by a Fourth Order PDE

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conference on image processing - algorithms and systems vii

作者： Ma, Wenhua You, Yu-Li Kaveh, M. Guangdong Univ Foreign Studies Sch Informat 2 Baiyundadao Guangzhou Guangdong Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)9780819474957

A fourth order PDE is proposed as the regularization operator for image restoration in order to alleviate the "blocky" effects that frequently mar restored images regularized with anisotropic diffusion ( a second order PDE). This is motivated by its desirable property of evolving toward an image consisting of piecewise planar areas which is a less blocky and better approximation to natural images. In order to mitigate speckle artifacts that it frequently brings about, image gradient magnitude is added as an additional variable of the nonlinearity function that controls its behavior. A numerical implementation method is presented and simulation results indicate that the proposed method tend to produce restored images which are smoother in smooth areas and sharper in feature-rich areas. However, speckle artifacts need to be carefully addressed.

关键词： image restoration Anisotropic diffusion Speckle image processing Point spread functions Cameras Control systems Computer simulations image analysis image compression

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