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Enhanced Object Detection and Classification via Multi-Method Fusion

Computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni jeongmin park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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Computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug jeongmin park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

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Computers, Materials & Continua 2024年第1期78卷 1381-1398页

作者： Aysha Naseer Nouf Abdullah Almujally Saud S.Alotaibi Abdulwahab Alazeb jeongmin park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information System Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their *** proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their ***,two distinct kinds of features are obtained from the segmented images to help identify the objects of *** Artificial Neural Network(ANN)is then used to recognize the objects based on their *** were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested *** findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

关键词： K-region fusion segmentation recognition feature extraction artificial neural network computer vision

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani jeongmin park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Vehicle Detection and Tracking in UAV Imagery via YOLOv3 and Kalman Filter

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Computers, Materials & Continua 2023年第7期76卷 1249-1265页

作者： Shuja Ali Ahmad Jalal Mohammed Hamad Alatiyyah Khaled Alnowaiser jeongmin park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-si15073Gyeonggi-doKorea

Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic *** academics have been drawn to this topic because of the challenges and the large variety of *** paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on *** is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not *** goal of this study is to develop a method that first extracts the region of interest(ROI),then finds and tracks the items of *** suggested system is divided into six *** photos from the obtained dataset are appropriately georeferenced to their actual locations in the first phase,after which they are all *** ROI,or road and its objects,are retrieved using the GrabCut method in the second *** third phase entails data *** segmented images’noise is eliminated using Gaussian blur,after which the images are changed to grayscale and forwarded to the following stage for additional morphological *** YOLOv3 algorithm is used in the fourth step to find any automobiles in the *** that,the Kalman filter and centroid tracking are used to perform the tracking of the detected *** Lucas-Kanade method is then used to perform the trajectory analysis on the *** suggested model is put to the test and assessed using the Vehicle Aerial Imaging from Drone(VAID)*** detection and tracking,the model was able to attain accuracy levels of 96.7%and 91.6%,respectively.

关键词： Kalman filter georeferencing object detection object tracking YOLO

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Tracking and Analysis of Pedestrian’s Behavior in Public Places

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Computers, Materials & Continua 2023年第1期74卷 841-853页

作者： Mahwish Pervaiz Mohammad Shorfuzzaman Abdulmajeed Alsufyani Ahmad Jalal Suliman A.Alsuhibany jeongmin park Department of Computer Science Bahria UniversityIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Air UniversityIslamabadPakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-si15073Gyeonggi-doKorea

Crowd management becomes a global concern due to increased population in urban *** management of pedestrians leads to improved use of public *** of pedestrian’s is a major factor of crowd management in public *** are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the *** this paper,we have proposed a new method for pedestrian’s behavior *** filter has been used to detect pedestrian’s usingmovement based ***,we have performed occlusion detection and removal using region shrinking method to isolate occluded *** verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each *** Wolf Optimizer(GWO)has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient(XG)Boost *** has been evaluated using pedestrian’s data from avenue and UBI-Fight datasets,where both have different *** mean achieved accuracies are 91.3%and 85.14%over the Avenue and UBI-Fight datasets,*** results are more accurate as compared to other existing methods.

关键词： Crowd management kalman filter region shrinking XG-Boost classifier

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Multiple Pedestrian Detection and Tracking in Night Vision Surveillance Systems

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Computers, Materials & Continua 2023年第5期75卷 3275-3289页

作者： Ali Raza Samia Allaoua Chelloug Mohammed Hamad Alatiyyah Ahmad Jalal jeongmin park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

Pedestrian detection and tracking are vital elements of today’s surveillance systems,which make daily life safe for ***,human detection and visualization have become essential inventions in the field of computer ***,developing a surveillance system with multiple object recognition and tracking,especially in low light and night-time,is still ***,we propose a novel system based on machine learning and image processing to provide an efficient surveillance system for pedestrian detection and tracking at *** particular,we propose a system that tackles a two-fold problem by detecting multiple pedestrians in infrared(IR)images using machine learning and tracking them using particle ***,a random forest classifier is adopted for image segmentation to identify pedestrians in an *** result of detection is investigated by particle filter to solve pedestrian *** the extensive experiment,our system shows 93%segmentation accuracy using a random forest algorithm that demonstrates high accuracy for background and roof ***,the system achieved a detection accuracy of 90%usingmultiple templatematching techniques and 81%accuracy for pedestrian ***,our system can identify that the detected object is a ***,our system provided the best results compared to the state-ofart systems,which proves the effectiveness of the techniques used for image segmentation,classification,and *** presented method is applicable for human detection/tracking,crowd analysis,and monitoring pedestrians in IR video surveillance.

关键词： Pedestrian detection machine learning segmentation tracking verification

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A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

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Computers, Materials & Continua 2023年第4期75卷 959-976页

作者： MZeeshan Sarwar Mohammed Hamad Alatiyyah Ahmad Jalal Mohammad Shorfuzzaman Nawal Alsufyani jeongmin park Department of Computer Science Air UniversityIslamabadPakistan Department of Computer Science College of Sciences and Humanities in AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

In the last decade,there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D *** enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given *** cues can improve the quality of object detection and *** main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D *** proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model(mWMM).mWMM is challenging to handle when the quality of the image is not ***,the proposed RGB-D-based system uses depth cues for segmentation with the help of *** it extracts multiple features from the segmented *** selected features are fed to the Artificial Neural Network(ANN)and Convolutional Neural Network(CNN)for detecting *** achieved 92.13%of mean accuracy over NYUv1 dataset and 90.00%of mean accuracy for the Redweb_v1 ***,their results are compared and the proposed model with CNN outperforms other state-of-the-art *** proposed architecture can be used in autonomous cars,traffic monitoring,and sports scenes.

关键词： Artificial intelligence convolutional neural network depth images interactive-object detection machine learning

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Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

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Computers, Materials & Continua 2022年第12期73卷 4763-4777页

作者： Mir Mushhood Afsar Shizza Saqib Yazeed Yasin Ghadi Suliman A.Alsuhibany Ahmad Jalal jeongmin park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

Virtual reality is an emerging field in the whole *** problem faced by people today is that they are more indulged in indoor technology rather than outdoor ***,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person *** system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the *** track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the ***,the sensor data is sent to a machine learning model,which detects the game played by the *** detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human *** main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting *** proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational *** experimentation proved the validity of the proposed system.

关键词： Artificial neural networks bluetooth connection inertial sensors machine learning virtual reality exergaming

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An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

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Computers, Materials & Continua 2022年第8期72卷 2597-2615页

作者： Yazeed Yasin Ghadi Nida Khalid Suliman A.Alsuhibany Tamara al Shloul Ahmad Jalal jeongmin park Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Human Activity Recognition(HAR)plays an important role in life care and health monitoring since it involves examining various activities of patients at homes,hospitals,or ***,the proposed system integrates Human-Human Interaction(HHI)and Human-Object Interaction(HOI)recognition to provide in-depth monitoring of the daily routine of *** propose a robust system comprising both RGB(red,green,blue)and depth *** particular,humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency *** track the movement of humans,we proposed orientation and thermal features.A codebook is generated using Linde-Buzo-Gray(LBG)algorithm for vector ***,the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network(ANN)while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for *** are two publicly available datasets used for experimentation on HHI recognition:Stony Brook University(SBU)Kinect interaction and the University of Lincoln’s(UoL)3D social activity ***,two publicly available datasets are used for experimentation on HOI recognition:Nanyang Technological University(NTU)RGB-D and Sun Yat-Sen University(SYSU)3D HOI *** results proved the validity of the proposed system.

关键词： Artificial neural network human-human interaction humanobject interaction k-ary tree hashing machine learning

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