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Motion Based Real-Time Siamese Multiple Object Tracker Model 2nd

Motion Based Real-Time Siamese Multiple Object Tracker Model

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2nd international conference on Cybersecurity and Evolutionary Data Engineering, ICCEDE 2022

作者： Kanaujia, Vishal Kumar Yadav, Satya Prakash Mishra, Himanshu Kumar, Awadhesh de Albuquerque, Victor Hugo C. Computer Science and Engineering-Data Science Department ABES Engineering College Uttar Pradesh Ghaziabad India 201306 Greater Noida India CE Fortaleza Brazil Department of CSE-APEX Chandigarh University Punjab Gharuan Mohali India Department of Computer Science and Engineering Kamala Nehru In-Stitute of Technology Sultanpur—Kadipur Rd Uttar Pradesh Sultanpur228118 India Department of Teleinformatics Engineering Federal University of Ceará CE Fortaleza Brazil

ISBN: (纸本)9789819950799

Motion multiple object tracker model is the objective of the object detection and automated monitoring of the scene for suspicious activity incidents on an ongoing basis. An algorithm tracks the motion of an object in order to predict or anticipate where it will be in relation to its trajectory. The identification method of moving objects using digital image processing techniques in a video series is known as video sequence object detection. The most common tracking method is video surveillance, which is also used in video control systems, intelligent road systems, intrusion monitoring, and airport safety. The tracker has to perform object matching from one edge to the next to get the desired result in the image area. The Identification of object is mainly carried out by the removal of frontal areas. The proposed methodology outlines the conditions under which a particular method yields the most effective results and indicates the location of detected objects for surveillance purposes. This article aims to develop appropriate image processing and computer vision algorithms involving multiple input video sequences and perturbing detection rate study that measures the forward pixel detection rate of background models for different color contrasts for use in road flow and monitoring. This algorithm uses a method that takes into account the pixel intensities of the image, but the results vary in terms of speed, memory requirements to provide accurate results in these situations, and significantly underestimate performance in all cases. Intelligent Transport Systems (ITS) have become more relevant around the world in today's overcrowded transport network and address persistent issues such as transport mobility and safety, in particular in metropolitan areas. Its main objectives are to improve the safety, transport comfort, and efficiency of road traffic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Object detection

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Research on the Application of computer vision Technology in the Adversarial Judgment System in Competitive Sports Competitions

Research on the Application of Computer Vision Technology in...

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Yong Tang Hunan University of Humanities Science and Technology Changsha Hunan China Hunan Normal University Changsha Hunan China

In recent years, computer vision technology has experienced a period of rapid development, and it has achieved remarkable results on core problems in various fields. At the same time, with the iterative development of 5G communication, mobile Internet, terminal equipment and other related technologies, massive amounts of video data will continue to be generated every minute and every second in the world. The large-scale video data has prompted the research carrier of computer vision to gradually develop from static pictures to video sequences, and the socially concerned objects are also shifting from target instance information in single-frame images to target motion information in consecutive multi-frame images. The automatic tracking technology of complex motion and posture based on machine vision technology effectively utilizes the image data processing ability of computer vision technology, and provides a strong data foundation for the application in the confrontation judgment system in competitive sports competitions.

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Ant Colony Optimization based Medical image Preservation and Segmentation 2

Ant Colony Optimization based Medical Image Preservation and...

引用

2nd IEEE international conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Nandihal, Priya Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Madhumala, R.B. Khanna, Ashish Sunil Kumar, V. Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Design Bangalore India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceara Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9781665493963

image segmentation is an essential component in many different types of computer vision systems. image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work. © 2022 IEEE.

关键词： Impulse noise

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Spatiotemporal Attention Network for Action Recognition Based on Motion Analysis

Spatiotemporal Attention Network for Action Recognition Base...

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Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Mingxiao Yu Siyu Quan Jiajia Wang Yunlong Li School of Computer Science and Technology Xinjiang University XJU Urumqi China School of Physics and Electronic Engineering Linyi University Linyi China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Action recognition in video content understanding primarily relies on the information extracted by the model, which needs to encompass both spatial and temporal features. Performance enhancement in deep learning models hinges on effectively learning these crucial spatial and temporal features from videos. Building on this analysis, we propose a spatiotemporal attention network for action recognition based on motion analysis(STAN-ARMA), comprising an optical flow integration module, an optical flow keyframe extraction module, and a spatial attention module. Experimental validation on the UCF-101, HMDB51, UTD-MHAD and HAA500 benchmark datasets demonstrates that the proposed network effectively learns spatial motion characteristics at critical moments in videos, thereby improving the model's recognition performance.

关键词： Deep learning image recognition Machine vision Fasteners Feature extraction Spatiotemporal phenomena Data mining Human activity recognition Motion analysis Optical flow

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Deepfake Creation Using Gans and Autoencoder and Deepfake detection

Deepfake Creation Using Gans and Autoencoder and Deepfake de...

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international conference on vision Towards Emerging Trends in Communication and Networking (ViTECoN)

作者： Manoj Kumar Das Manav Kumar Ishank Kumar Kapil Rajesh Kumar Yadav Dept. of computer engineering Delhi technological university Delhi India

Deep learning techniques is useful in a myriad of applications such as computer vision processing, natural language processing, as well as deepfake detection. The development of deep learning algorithms for imaging detection has resulted in the development of deepfakes. These fakes employ advanced algorithms for deep learning to generate fake images that are extremely difficult to differentiate from authentic images. In a fake video, the face, expression or speech is replaced with an image of another's face, with a distinct speech or emotion with the help of the technology of deep learning. These videos are typically so sophisticated that the traces of manipulation are hard to spot. Social media are among the most frequently targeted and most serious because they are vulnerable platforms that are susceptible to blackmailing or making a person look bad. There are several existing efforts to detect fake images, but there have been very few efforts developed for video content on social media.

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Advanced Glaucoma detection using CNN models integrated with random forest classifier for enhanced diagnosis

Advanced Glaucoma detection using CNN models integrated with...

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2nd international conference on computer vision and Internet of Things (ICCVIoT 2024)

作者： Manikanda Prabu Nallasivam Brindha S Vijayalakshmi N Thirrunavukkarasu R R Jegan S Rajasekar P Electronics and Communication Engineering Sri Krishna College of Technology Coimbatore India Electronics and Communication Engineering Nandha Engineering College Erode India Computer Science and Engineering Alliance School of Advanced Computing Bangalore India Computer and Communication Engineering Sri Eshwar College of Engineering Coimbatore India Electronics and Instrumentation Engineering Kongu Engineering College Erode India

ISBN: (数字)9781837242535

Glaucoma is a prevalent retinal disease worldwide, leading to irreversible blindness if not diagnosed early. This research aims to distinguish glaucomatous eyes from normal eyes using an innovative glaucoma detection framework that integrates a Random Forest classifier with VGG-19 and ResNet50 models. The proposed method aggregates classification outcomes from Random Forest, VGG-19, and ResNet50 models through a post-processing decision block to deliver the final classification. This approach addresses limitations of existing techniques, significantly enhancing diagnostic accuracy and robustness by leveraging the strengths of these models. The framework, developed through ensemble modeling, achieves an accuracy of 96.10%, a precision rate of 99.10%, a recall rate of 89.00%, and an F1-score of 94.20%. Our research underscores the importance of integrating conventional optic disc measurement methods with image shape-derived features in machine learning models, enabling early and precise glaucoma diagnosis.

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Differential Evolution Algorithm based on Grid Entropy and Bessel Mutation Strategy 2

Differential Evolution Algorithm based on Grid Entropy and B...

引用

2nd international conference on Advanced Algorithms and Signal image processing, AASIP 2022

作者： Xiong, Ying Liu, Gang Xiao, Yitao Hubei University of Technology School of Computer Science Hubei Wuhan430068 China

ISBN: (纸本)9781510660571

In order to further improve the exploration and exploitability of Differential Evolution (DE) algorithm, Bessel mutation strategy based on grid entropy is proposed to improve iLSHADE-RSP algorithm. The new algorithm is called BiLSHADE-RSP. It has the following features: (1) A grid entropy for observing the convergence of the population is proposed to be used as a signal of applying Bessel mutation strategy;(2) A mutation strategy based on Bessel curve is designed to enhance the exploitability of DE. BiLSHADE-RSP is compared with five algorithms, namely iLSHADE-RSP, LSHADE-RSP, L-SHADE, DCDE and FNADE. We use CEC2017 test suit to verify the performance of the proposed algorithm. Experimental results show that the improvement in BiLSHADE-RSP algorithm has achieved the best effect on complex test functions, and its solution accuracy is better than the current popular DE variants. © 2022 SPIE.

关键词： Entropy

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HRNet-U: Combining the Strengths of U-Net and HRNet for Efficient Pose Estimation

HRNet-U: Combining the Strengths of U-Net and HRNet for Effi...

引用

Algorithm, image processing and Machine vision (AIPMV), international conference on

作者： Biao Guo Fangmin Guo Qian He Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

High-resolution network (HRNet) shows excellent performance in 2D multi-person pose estimation tasks. It can maintain high-resolution representation in the network and exchange information between multi-scale features, significantly improving the ability of key point prediction. However, this multiscale fusion strategy leads to computational redundancy and increases processing delays, limiting its application to resource-constrained edge devices. To solve this problem, this paper proposes a new lightweight network structure (HRNet-U), which combines the high resolution parallel advantage of HRNet and the computational efficiency of U-Net, and adopts the U-Net structure for multiscale feature fusion at each stage. Through this design, HRNet-U still maintains high resolution branch and multi-scale information, which is more suitable for the application scenarios of edge devices. This model achieves comparable or even better performance on the COCO and MPII validation sets.

关键词： Performance evaluation Adaptation models Pose estimation Redundancy computer architecture Feature extraction Robustness Real-time systems Delays Computational efficiency

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image Captioning Using VGG-16 Deep Learning Model

Image Captioning Using VGG-16 Deep Learning Model

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Disruptive Technologies (ICDT), international conference on

作者： Vishal Jayaswal Sharma Ji Satyankar Vanshika Singh Yuvika Singh Vaibhav Tiwari Department of CSE Ajay Kumar Garg Engineering College Ghaziabad India

image captioning is a method that creates captions from images. It makes use of computer vision, natural language processing, and deep learning. The field of image captioning has evolved significantly in recent times, thanks to the application of both conventional and sophisticated deep learning approaches. This study uses two well-known benchmark datasets, Flickr8k and Flickr30k, to examine the use of the VGG-16 convolutional neural network (CNN) for producing descriptive captions. A re- current neural network (RNN) is integrated to generate coherent and contextually relevant captions after the VGG-16 model is utilized for feature extraction. Human-provided references and generated captions are compared for quality using standard assessment metrics such as BLEU. The findings have applicationsin the areas of content indexing, assistive technology for the visually impaired, and enhancing user interfaces on image-centric systems. The comparison of the Flickr8k and Flickr30k datasets sheds light on the challenges posed by the different datasets and provides guidance for future image captioning research. A list of references, a synopsis of the key findings, and suggestions for future research topics are included in the paper's conclusion.

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Real-time crop analysis with enhanced image classification using deep learning and fuzzy logic techniques

Real-time crop analysis with enhanced image classification u...

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2nd international conference on computer vision and Internet of Things (ICCVIoT 2024)

作者： Kanimozhi S Deepika Roselind J Neal Shah Sai Ramesh L School of Computer Science and Engineering VIT University Chennai India School of Electronics Engineering VIT University Chennai India Department of Computer Science and Engineering St. Joseph's Institute of Technology Chennai India

ISBN: (数字)9781837242535

The proposed research presents a smart irrigation system is designed to optimize agricultural water management using a robotic framework that integrates deep learning and fuzzy logic for real-time crop classification and decision-making. It employs a combination of VGG-19 and a fusion model RTCropNet (Real-time crop network), leveraging the strengths of each architecture: VGG-19 for robust feature extraction and the RTCropNet fusion for enhanced spatial localization and fine-grained crop classification. Pre-processing is performed through advanced fuzzy logic techniques to improve image quality, enhancing classification accuracy across diverse crop types. Evaluation metrics indicate that the RTCropNet model achieves a training accuracy of 80.15% and a validation accuracy of 81.77%, with precision, recall, and F1 scores of 0.83, 0.82, and 0.82, respectively, outperforming traditional methods on complex agricultural imagery. Comparative analysis shows the model's superiority over EfficientNet-B3, DenseNet-121, and MobileNetV2, particularly in handling high-resolution spatial details, while maintaining efficiency in real-time conditions. This integration of deep learning with fuzzy logic and sensor data represents a novel approach in precision irrigation, offering a dynamic, data-driven response system that adjusts irrigation based on real-time environmental conditions, distinguishing it from conventional SOTA approaches.

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