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Multiple Face Detection and Recognition for System-an-Chip FPGAs 21

Multiple Face Detection and Recognition for System-an-Chip F...

引用

21st International Joint Conference on Computer Science and Software Engineering, JCSSE 2024

作者： Santiwiwat, Siraphop Hajian, Amir Ruangsang, Watchara Aramvith, Supavadee Chulalongkorn University Multimedia Data Analytic and Processing Research Unit Faculty of Engineering Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350381764

Recognizing multiple faces within a single frame or image presents a significant challenge in facial recognition tasks. This challenge demands robust algorithms capable of handling variations in facial position, unstable brightness, and the presence of accessories such as glasses or hats. However, in current surveillance applications, multiple face recognition is vital for maintaining various security measures. In such applications, there is a need for processors that are compact and deployable in diverse environments. FPGA is popular due to its compact size, cost-effectiveness, and high performance. This paper presents the implementation of multiple face detection and recognition on an SoC FPGA utilizing the Xilinx Kria KV260 platform. We chose the MTCNN algorithm for multiple face detection and the ResNet-20 model for multiple face recognition developed by Vitis AI, and we fine-tuned this model using the CUFace dataset. Our results demonstrate the robust capability to detect and recognize faces of up to 8 individuals within a single frame in just 0.11 seconds, with a recognition rate of 93.77%. © 2024 IEEE.

关键词： System-on-chip

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2D T1-Weighted CE MRI Images Classification with Hybrid Channel Attention Block enhanced VGG-16 and MobileNet 1

2D T1-Weighted CE MRI Images Classification with Hybrid Chan...

引用

1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Fareed, Mohammad Jawad Pluempitiwiriyawej, Charnchai Murtaza, Ghulam Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Department of Electircal Engineering Bangkok Thailand

ISBN: (纸本)9798350373042

Motivated by the imperative need for precise and swift classification of brain tumors depicted in MRI images, this study unveils a novel classification network tailored explicitly for this task. Existing lightweight methodologies, although explored extensively, have fallen short of achieving the accuracy benchmarks attained by the model. The approach underwent rigorous testing on a challenging 2D T1-weighted CE-MRI dataset housing three distinct types of brain tumors: Meningioma, Glioma, and Pituitary. Recognizing the susceptibility of models to overfitting during training, introduced was a normalized combined channel and spatial attention mechanism, strategically incorporated to serve as an effective regularizer. Comparative analysis against state-of-the-art methodologies on this dataset highlights a notable performance boost of 2.12 percentage points achieved by integrating this attention mechanism into a base network. Furthermore, the fusion of the model with the pre-trained VGG16 network and GoogleNet into an ensemble demonstrates superior accuracy;however, this amalgamation comes at the expense of execution speed. © 2024 IEEE.

关键词： Image classification

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Multiple Face Detection and Recognition for System-an-Chip FPGAs

Multiple Face Detection and Recognition for System-an-Chip F...

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International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Siraphop Santiwiwat Amir Hajian Watchara Ruangsang Supavadee Aramvith Department of Electrical Engineering Multimedia Data Analytic and Processing Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

关键词： Program processors Image recognition Face recognition Surveillance Face detection Security Task analysis

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YOLO for Small Objects in Aerial Imagery: A Performance Evaluation

YOLO for Small Objects in Aerial Imagery: A Performance Eval...

引用

International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Wimonthip Saenprasert Ei Ei Tun Amir Hajian Watchara Ruangsang Supavadee Aramvith Department of Electrical Engineering Multimedia Data Analytic and Processing Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

Unmanned aerial vehicles (UAVs) offer significant advantages in accessing remote or high-risk locations. Yet, they also present challenges concerning privacy and safety. Effective air traffic management is crucial for ensuring compliance with security regulations. The primary objective is to monitor the flight trajectories of target objects in the airspace, necessitating efficient object detection, especially for distant objects, to facilitate surveillance and preparedness for addressing potential threats. However, the current performance of existing models in detecting small objects is inadequate for real-time surveillance applications. This study advocates prioritizing object detection over precise prediction of object bounding box sizes. We propose a novel approach where the efficacy of object detection is measured by the ability to identify small objects. This research serves as an additional variable for evaluating and enhancing models designed for small object detection. We selected several versions of the YOLO model and assessed them on the COCO val 2017 dataset, counting the number of accurately predicted objects. Based on the most popular drone size of 100–400 pixels, YOLOv5-x is best suited for the future development of smaller objects. However, because the model is large, we can choose the YOLOv5-s, which is the smaller version. YOLOv5-s detects small objects in the same way that the newest version of the YOLO family, the YOLOv9-e. By measuring this, we may establish the model's capabilities and select a model appropriate for the target object's size.

关键词： YOLO Accuracy Computational modeling Surveillance Prototypes Real-time systems Trajectory

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Efficient Deep Attentive Pixels Network in Face Super-Resolution at Scale Factor of 16 20

Efficient Deep Attentive Pixels Network in Face Super-Resolu...

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20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Aung, Hein Htet Aramvith, Supavadee Faculty of Engineering Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350310467

Nowadays, Face Super-Resolution (FSR) models utilize the fusion approach, which combines the attention technique with the super-resolution network. The fusion approach has been proposed and solves the problem of FSR. Facial attributes have been effectively used to guide low-level information of the face to perform robust face image restoration. Iterative techniques appraised the value of facial landmarks to boost the reconstruction capability of the super-resolution network. Nevertheless, the network parameters in FSR are high, while the learning rate is still low. This paper proposes an attention mechanism combined with the Face Alignment Network (FAN). The proposed attention mechanism consists of channel attention and a non-local module. Our proposed model outperforms at the scale of $\times 16$ compared to the other state-of-the-art models. © 2023 IEEE.

关键词： Image reconstruction

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An Attention-Guided Image Super-Resolution for Scene Text 8

An Attention-Guided Image Super-Resolution for Scene Text

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8th International Conference on Video and Image processing, ICVIP 2024

作者： Em, Tithnorakneath Aramvith, Supavadee Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

Scene Text Image Super-Resolution (STISR) has become a significant pre-processing technique to enhance text recognition accuracy in low-resolution scene text images, particularly when addressing the challenges of low-resolution (LR) images in real-world applications. Despite advancements in general and text-focused methods, current techniques often fail to retain the fine-grained and text-specific details necessary for downstream recognition tasks. This paper presents a novel Attention-Guided Scene Text Image Super-Resolution (AG-STISR) framework designed to overcome these challenges. Our approach integrates advanced channel attention and spatial attention mechanisms within a shallow feature extraction module, enabling precise enhancement of key text features such as character edges, contours, and fine structural details. Channel attention helps prioritize the features rich in text-specific details by focusing the model's attention on the channels with the most important data for text recognition. On the other hand, Spatial attention emphasizes locating and highlighting the most significant spatial areas in the image, like character outlines and strokes, ensuring that key elements are maintained while minimizing background distractions. We extensively evaluated our model on the TextZoom dataset and assessed it against various scene text recognition standards. The results demonstrate that our AG-STISR model consistently outperforms state-of-the-art methods, showing significant improvements in PSNR, SSIM, and text recognition accuracy across varying levels of image degradation. This research introduces a compelling solution to STISR, enhancing not only image clarity but also the reliability of text recognition in real-world applications. © 2025 SPIE.

关键词： Image enhancement

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Machine Learning-Based Classification of Mental Health State Using the DASS-21 Profile 16

Machine Learning-Based Classification of Mental Health State...

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16th Biomedical Engineering International Conference, BMEiCON 2024

作者： Duangchaemkarn, Khanita Khammarew, Patiphan Aramvith, Supavadee School of Pharmaceutical Sciences University of Phayao Department of Pharmacy Practice Phayao56000 Thailand School of Pharmaceutical Sciences University of Phayao Pharmacy Research Extern Under the Doctor of Pharmacy Program Phayao56000 Thailand Chulalongkorn University Multimedia Data Analytic and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Bangkok10330 Thailand

ISBN: (纸本)9798331505431

This study aimed to determine the most effective machine learning model for classifying emotional states of depression, anxiety, and stress based on the DASS-21 profile. Six machine learning models were developed and tested using 2,602 records from the University of Phayao Counselling Center, collected between 23 July and 16 August 2020. The results demonstrate that the deep learning model provided the highest accuracy and precision, exceeding 90% for all three emotional categories, with an AUC greater than 99%. These findings introduce an innovative approach for classifying emotional states of depression, anxiety, and stress using the DASS-21 profile as a benchmark. Incorporating additional features into the model could further enhance its utility in clinical decision-making, improving the accuracy of mental health screening and diagnosis. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning-Based Golf Swing Sequence Analysis

Deep Learning-Based Golf Swing Sequence Analysis

引用

IEEE Region 10 International Conference TENCON

作者： Amir Hajian Karit Sookpreedee Kingrak Phairoh Watchara Ruangsang Supavadee Aramvith Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Multimedia Data Analytic and Processing Research Unit Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Multimedia Data Analytics and Processing Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

Golf is widely recognized as one of the most popular sports globally. However, one drawback of playing golf is the relatively high cost of equipment and coaching. While numerous training programs are available to assist players in their practice, there is currently no swing analysis program developed by Thai professionals. In this project, advanced deep learning models were employed: SwingNet, capable of predicting the sequence of eight golf swing events in videos and determining the confidence level of each swing, and MoveN et, designed to identify joint positions on the body and represent them as skeletons. These models were integrated into a customized template-matching algorithm that utilized angle-based measurements to analyze the sequence of golf swings. This analysis assessed the similarity score, represented as a percentage, between two individuals for each golf swing event. Furthermore, various techniques were implemented to enhance the efficiency of SwingN et. Through performance evaluation, it was observed that the efficiency of SwingN et surpassed by one percent compared to the pre-trained model.

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Deep Learning-Based Golf Swing Sequence Analysis 38

Deep Learning-Based Golf Swing Sequence Analysis

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38th IEEE Region 10 Conference, TENCON 2023

作者： Hajian, Amir Sookpreedee, Karit Phairoh, Kingrak Ruangsang, Watchara Aramvith, Supavadee Chulalongkorn University Department of Electrical Engineering Bangkok Thailand Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Faculty of Engineering Department of Electrical Engineering Bangkok10330 Thailand

ISBN: (纸本)9798350302196

关键词： Golf

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Non-Local Technique on Deep Attentive Face Super-Resolution Network 22

Non-Local Technique on Deep Attentive Face Super-Resolution ...

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22nd International Symposium on Communications and Information Technologies, ISCIT 2023

作者： Hajian, Amir Aung, Hein Htet Ruangsang, Watchara Chen, Sovann Aramvith, Supavadee Chulalongkorn University Department of Electrical Engineering Bangkok Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9781665457316

Recent Face Super-resolution (FSR) based on iterative collaboration between facial image recovery network and landmark estimation has succeeded in super-resolving facial images. However, the existing noise in coarse features at the low-level feature extraction leads to inaccurate facial priors such as landmarks and component maps, consequently degrading the super-resolved face image on a large scale. This paper proposes, a Non-local technique for deep attentive face super-resolution network (NLDA). A Non-local module has been designed before the residual channel attention block (RCAB) to eliminate noise degradation on coarse features effectively. The proposed model optimizes feature extraction and improves facial landmark fusion to yield higher-quality super-resolved images. This approach facilitates more accurate landmark estimation and boosts the performance of our model on a large scale and various face poses. Quantitative and qualitative experiments over CelebA and Helen face image datasets show that the proposed method outperforms other state-of-the-art FSR methods in recovering high-quality face images in various face poses and at a large scale. © 2023 IEEE.

关键词： Feature extraction

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