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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Research on Detection Algorithm of Hot Work Based on Target recognition and Image Processing 4

Research on Detection Algorithm of Hot Work Based on Target ...

引用

4th ieee International conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Zhao, Guinan Li, Lei Liu, Xiaodong Zhao, Ran Tang, Wenjie Gan, Fangji Shandong Hi-Speed Construction Management Group Co. Ltd Shandong Jinan250001 China Technology Co. Ltd Sichuan Chengdu610051 China Sichuan University Sichuan Chengdu610065 China

ISBN: (纸本)9798350359589

To enable real-time monitoring of fire operations within construction sites and to reduce the chance of fires, this paper proposes a detection algorithm that incorporates target recognition and image processing. Firstly, we add an attention mechanism to YOLO V7 to increase the localization accuracy of highlighted areas. Secondly, we use an image processing algorithm to identify the presence of fire in the localized area and recheck the localized highlighted area to improve the detection accuracy. After the verification is complete, this paper proposes a method based on monocular visual ranging to detect the presence of staff around the fire light to further confirm the authenticity of the fire operation and finally complete the detection of the fire moving operation. To verify the reliability and recognition accuracy of the algorithm, experiments were conducted on the dataset collected at the construction site, including ablation experiments on different target recognition networks and the application of different attention mechanisms. In addition to experiments on image segmentation of different highlighted areas located, and finally, extensive verification experiments on monocular visual ranging experiments are carried out. The experimental results show that the algorithm enables an effective detection of fire operations. © 2024 ieee.

关键词： Attention Mechanism Fire operation Image processing monocular vision Object detection network

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Unsupervised Learning for Robust Fitting: A Reinforcement Learning Approach

Unsupervised Learning for Robust Fitting: A Reinforcement Le...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Truong, Giang Le, Huu Suter, David Zhang, Erchuan Gilani, Syed Zulqarnain Edith Cowan Univ Sch Sci Churchlands WA Australia Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden

ISBN: (纸本)9781665445092

Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for datasets highly contaminated with outliers is, however, still challenging due to the underlying computational complexity. Recent literature has focused on learning-based algorithms. However, most approaches are supervised (which require a large amount of labelled training data). In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting. Unlike other methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasi-convex residuals. We empirically show that our method outperforms existing unsupervised learning approaches, and achieves competitive results compared to traditional methods on several important computer vision problems(1).

关键词： computer vision Structure from motion Computational modeling Fitting Estimation Training data Reinforcement learning

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Fair and accurate age prediction using distribution aware data curation and augmentation 22

Fair and accurate age prediction using distribution aware da...

引用

22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Cao, Yushi Berend, David Tolmach, Palina Amit, Guy Levy, Moshe Liu, Yang Shabtai, Asaf Elovici, Yuval Nanyang Technol Univ Singapore Singapore Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9781665409155

Deep learning-based facial recognition systems have experienced increased media attention due to exhibiting unfair behavior. Large enterprises, such as IBM, shut down their facial recognition and age prediction systems as a consequence. Age prediction is an especially difficult application with the issue of fairness remaining an open research problem (e.g., predicting age for different ethnicity equally accurate). One of the main causes of unfair behavior in age prediction methods lies in the distribution and diversity of the training data. In this work, we present two novel approaches for dataset curation and data augmentation in order to increase fairness through balanced feature curation and increase diversity through distribution aware augmentation. To achieve this, we introduce out-of-distribution detection to the facial recognition domain which is used to select the data most relevant to the deep neural network's (DNN) task when balancing the data among age, ethnicity, and gender. Our approach shows promising results. Our best-trained DNN model outperformed all academic and industrial baselines in terms of fairness by up to 4.92 times and also enhanced the DNN's ability to generalize outperforming Amazon AWS and Microsoft Azure public cloud systems by 31.88% and 10.95%, respectively.

关键词： computer vision Cloud computing Codes Face recognition Training data Prediction methods Benchmark testing

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Deep Flower: A Deep Learning Approach for Accurate Flower Classification 3

Deep Flower: A Deep Learning Approach for Accurate Flower Cl...

引用

International conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Soundarya, M. Kannan, Muthu Vishal R. Saravanan, T. D. Praveen, V Vinora, A. Velammal Coll Engn & Technol Dept Informat Technol Madurai Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Flower classification is a challenging task in computer vision, requiring models to discern subtle visual differences among a vast array of floral species. In this project, we propose a novel approach to flower classification leveraging the vision Transformer (ViT) algorithm, a cutting-edge deep learning architecture that has demonstrated remarkable success in image recognition tasks. The ViT model replaces traditional convolutional layers with self-attention mechanisms, allowing it to capture long-range dependencies and global context in images more *** methodology involves pre-processing a comprehensive dataset of floral images, extracting features using ViT, and training a robust classification model. The dataset encompasses a diverse range of flowers, capturing variations in color, shape, and size. The ViT model's attention mechanisms enable it to learn hierarchical representations, improving its ability to differentiate between subtle visual nuances characteristic of different flower *** conduct extensive experiments to evaluate the proposed approach's performance, comparing it with traditional convolutional neural networks (CNNs) commonly used in flower classification tasks. Additionally, we explore techniques for model interpretability, shedding light on the decision-making process of the ViT-based *** results demonstrate the effectiveness of the vision Transformer in flower classification, surpassing the performance of conventional CNNs. The ViT model exhibits enhanced generalization capabilities and robustness to variations in illumination and background. Furthermore, our interpretability analysis provides insights into the discriminative features learned by the ViT model, contributing to a better understanding of its decision-making *** study not only advances the realm of computer vision but also provides valuable insights applicable in agriculture, horticulture, and ecological monitoring. The constructed model highl

关键词： Deep Learning Convolutional Neural Networks (CNNs) Flower Classification Image Processing computer vision Botany and vision Transformer

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Hyperdimensional computing as a framework for systematic aggregation of image descriptors

Hyperdimensional computing as a framework for systematic agg...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Neubert, Peer Schubert, Stefan Tech Univ Chemnitz Chemnitz Germany

ISBN: (纸本)9781665445092

Image and video descriptors are an omnipresent tool in computer vision and its application fields like mobile robotics. Many hand-crafted and in particular learned image descriptors are numerical vectors with a potentially (very) large number of dimensions. Practical considerations like memory consumption or time for comparisons call for the creation of compact representations. In this paper, we use hyperdimensional computing (HDC) as an approach to systematically combine information from a set of vectors in a single vector of the same dimensionality. HDC is a known technique to perform symbolic processing with distributed representations in numerical vectors with thousands of dimensions. We present a HDC implementation that is suitable for processing the output of existing and future (deep learning based) image descriptors. We discuss how this can be used as a framework to process descriptors together with additional knowledge by simple and fast vector operations. A concrete outcome is a novel HDC-based approach to aggregate a set of local image descriptors together with their image positions in a single holistic descriptor. The comparison to available holistic descriptors and aggregation methods on a series of standard mobile robotics place recognition experiments shows a 20% improvement in average performance and > 2x better worst-case performance compared to runner-up.

关键词： computer vision Systematics Aggregates Semantics Memory management Layout Tools

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Design and Implementation of Anti-Peeping Screen Algorithm Based on Soft-NMS YOLOv7 7

Design and Implementation of Anti-Peeping Screen Algorithm B...

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7th International conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Baining Chen, Hao Chen, Junlong Dai, Huawei School of Integrated Circuit Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Huizhou University Huizhou China

ISBN: (纸本)9798350350890

The occurrence of privacy breaches through screen peeping has highlighted the increasing importance of anti-peeping screen algorithms. In the traditional YOLOv7 algorithm, it may not accurately detect faces when individuals are mutually obscured. In order to improve the success rate of face detection, we propose a new anti-peeping screen algorithm, Soft-NMS-YOLOv7. Compared to the traditional NMS-YOLOv7, Soft-NMS-YOLOv7 prevents face leakage by specially handling the scores of adjacent detection boxes. Therefore, Soft-NMS-YOLOv7 exhibits better detection performance and robustness, increasing the accuracy of face detection in anti-peeping screen algorithms from 90.05% to 94.71%. Additionally, we have developed a physical model of Soft-NMS-YOLOv7 based on Raspberry Pi 4B. © 2024 ieee.

关键词： Face recognition

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EBSR: Feature Enhanced Burst Super-Resolution with Deformable Alignment

EBSR: Feature Enhanced Burst Super-Resolution with Deformabl...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Luo, Ziwei Yu, Lei Mo, Xuan Li, Youwei Jia, Lanpeng Fan, Haoqiang Sun, Jian Liu, Shuaicheng Megvii Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9781665448994

We propose a novel architecture to handle the problem of multi frame super-resolution (MFSR). The proposed framework is known as Enhanced Burst Super-Resolution (EBSR), which divides the MFSR problem into three parts: alignment, fusion, and reconstruction. We propose a Feature Enhanced Pyramid Cascading and Deformable convolution (FEPCD) module to align multiple low-resolution burst images in the feature level. And then the aligned features are fused by a Cross Non-Local Fusion (CNLF) module. Finally, the SR image is reconstructed by the Long Range Concatenation Network (LRCN). In addition, we build a cascading residual pathway structure (CR) to improve the performance. We conduct several experiments to analyze and demonstrate these modules. Our EBSR model won the champion in the real track and second place in the synthetic track in the NTIRE21 Burst Super-Resolution Challenge.

关键词： Training computer vision Correlation Tracking Convolution conferences Superresolution

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LoL-V2T: Large-Scale Esports Video Description Dataset

LoL-V2T: Large-Scale Esports Video Description Dataset

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tanaka, Tsunehiko Simo-Serra, Edgar Waseda Univ Tokyo Japan

ISBN: (纸本)9781665448994

Esports is a fastest-growing new field with a largely online-presence, and is creating a demand for automatic domain-specific captioning tools. However, at the current time, there are few approaches that tackle the esports video description problem. In this work, we propose a large-scale dataset for esports video description, focusing on the popular game "League of Legends". The dataset, which we call LoL-V2T, is the largest video description dataset in the video game domain, and includes 9,723 clips with 62,677 captions. This new dataset presents multiple new video captioning challenges such as large amounts of domain-specific vocabulary, subtle motions with large importance, and a temporal gap between most captions and the events that occurred. In order to tackle the issue of vocabulary, we propose a masking the domain-specific words and provide additional annotations for this. In our results, we show that the dataset poses a challenge to existing video captioning approaches, and the masking can significantly improve performance. Our dataset and code is publicly available(1).

关键词： Training Vocabulary computer vision Video description conferences Focusing Games

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Research on Image recognition System Based on computer Artificial Intelligence Technology 4

Research on Image Recognition System Based on Computer Artif...

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4th ieee International conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Xu, Zhihua School of Design and Art Jingdezhen Ceramic University Jingdezhen China

ISBN: (纸本)9798350359589

This paper uses mobile network mode to develop and model Python crawler based on Ubuntu16.04 and CUDA9.0. The visualization of this pattern was achieved using Tensor Board software. The hardware design of the system consists of three modules: linear array CCD sensor, DSP processor and signal processing circuit. Through the reconstruction and learning of such models, the trained models are stored, so as to obtain higher precision image recognition. A method of classifying objects using Fourier description operators is proposed. The experimental results show that the accuracy of the image is 96.8% and the recognition time is 0.55 seconds. Motion tracking is based on the positioning and capture of the specific position of the human hand in each screen, and the captured frame rate is 28 frames per second. The system can quickly acquire images of 1024∗1500 pixels at a rate of 5 photos/second. Through the static identification of posture and tracking of motion trajectory, the interaction between human and machine is better. © 2024 ieee.

关键词： artificial intelligence digital signal processing fourier Image identification technology mobile network

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A Unified Framework for Dangerous Situation Detection by Integrating Weapon Detection and Distressed Face Analysis

A Unified Framework for Dangerous Situation Detection by Int...

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2024 ieee International conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Narayanan, Swathi Jamjala Perumal, Boominathan Mandal, Siddarth Mohandas, Gayathri Jain, Naman Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331533311

The escalating prevalence of violent threats necessitates proactive security measures to safeguard human life. Within the realm of artificial intelligence (AI), computer vision has emerged as a pivotal tool, leveraging sophisticated algorithms to substantially alleviate the burden on human resources. In this research work, we propose a novel approach for dangerous situation detection that combines object detection and facial emotion recognition. This work includes detecting guns, knives, blood, alcohol, insulting gestures, and cigarettes as these are considered as items that are commonly associated with potential threats or dangerous situations in various contexts, such as crime scenes, conflict zones, or public safety scenarios. To achieve this, the harmful object detection model employs YOLOv8, which has been successful based on the mAP metric. The proposed work also incorporates a CNN model that relies on facial emotions for indicators of distress, fear, or discomfort, enabling the system to identify potential threats even in scenarios devoid of harmful objects. The results obtained demonstrate YOLOv8's superiority over a two-stage detector, and the Facial Expression recognition (FER) CNN achieves 7 9% test accuracy and 8 4% validation accuracy. The unified framework enhances environmental threat detection, contributing to effective risk mitigation. © 2024 ieee.

关键词： Emotion recognition

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