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

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Over-the-Air Adversarial Flickering Attacks against Video recognition Networks

Over-the-Air Adversarial Flickering Attacks against Video Re...

引用

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

作者： Pony, Roi Naeh, Itay Mannor, Shie Technion Israel Inst Technol Dept Elect Engn Haifa Israel Rafael Adv Def Syst Ltd Haifa Israel Nvidia Res Shanghai Peoples R China

ISBN: (纸本)9781665445092

Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers and video classifiers is that the latter usually use temporal information contained within the video. In this work we present a manipulation scheme for fooling video classifiers by introducing a flickering temporal perturbation that in some cases may be unnoticeable by human observers and is implementable in the real world. After demonstrating the manipulation of action classification of single videos, we generalize the procedure to make universal adversarial perturbation, achieving high fooling ratio. In addition, we generalize the universal perturbation and produce a temporal-invariant perturbation, which can be applied to the video without synchronizing the perturbation to the input. The attack was implemented on several target models and the transferability of the attack was demonstrated. These properties allow us to bridge the gap between simulated environment and real-world application, as will be demonstrated in this paper for the first time for an over-the-air flickering attack.

关键词： Deep learning computer vision Perturbation methods Observers pattern recognition Image classification

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Research on Text recognition of Natural Scenes for Complex Situations 3

Research on Text Recognition of Natural Scenes for Complex S...

引用

3rd International conference on pattern recognition and Machine Learning, PRML 2022

作者： Yu, Wenhua Ibrayim, Mayire Hamdulla, Askar Xinjiang University School of Information Science and Engineering Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

As a great invention that affects human development and progress, writing plays an essential role in human life and learning and the inheritance and development of culture. For thousands of years, the inheritance of Chinese culture has been inseparable from the writing records. Papermaking and printing have played a crucial role in the development of the times. Text extraction and recognition technology in natural scenes, that is, text recognition from photos or videos, has become a popular research direction in computer vision in recent years. Scene text recognition is a fundamental task in computer vision. It is often a critical step in many text-related tasks, such as document retrieval, card recognition, license plate recognition, and other tasks. The vital difference between the text recognition process and text detection is that the text in the photo or video needs to be recognized. Detection and recognition are mainly divided into two steps: locating text areas in photos or videos, that is, finding words or text lines of the bounding box;then the positioned text is recognized. Combining these two steps results in End-to-end recognition of text. The continuous development of scene text detection and recognition algorithm system will lay the foundation for the research of recognition problems such as multilingual recognition of scene text and formula recognition. © 2022 ieee.

关键词： computer vision

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Face recognition Attendance Management System Using Python 1

Face Recognition Attendance Management System Using Python

引用

1st ieee International conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Tiwari, Amit Kr. Dwivedi, Ashish Dubey, Arpit Kushwaha, Ashutosh Dubey, Devesh Kumar Tiwari, Harsh United Institute of Technology Computer Science and Engineering Prayagraj India United Institute of Technology Information Technology Prayagraj India

ISBN: (纸本)9798350356816

Facial recognition systems are incredibly important in today's digital world, spanning various industries. It has several benefits and Plays a crucial part in identifying individuals, authentication, and security. Face recognition is a frequently used technology since it is non-invasive and contactless, even if its accuracy is not as high as that of fingerprint and iris recognition. One noteworthy application is to replace the laborious and timeconsuming manual processes that are prone to proxy attendance difficulties in school, college, and office attendance marking [1]. The four stages of the proposed system are database generation, face identification using the Local Binary pattern Histogram technique, face detection using the Haar-Cascade classifier, and attendance updating. Student photos are used to create the database, while live streaming video in the classroom is used to capture real-time facial. © 2024 ieee.

关键词： Video streaming

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recognition of Defective Mineral Wool Using Pruned ResNet Models 21

Recognition of Defective Mineral Wool Using Pruned ResNet Mo...

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ieee 21st International conference on Industrial Informatics (INDIN)

作者： Rafiei, Mehdi Tran, Dat Thanh Iosifidis, Alexandros Aarhus Univ Dept Elect & Comp Engn DIGIT Aarhus Denmark Tampere Univ Dept Comp Sci Tampere Finland

ISBN: (纸本)9781665493130

Mineral wool production is a non-linear process that makes it hard to control the final quality. Therefore, having a non-destructive method to analyze the product quality and recognize defective products is critical. For this purpose, we developed a visual quality control system for mineral wool. X-ray images of wool specimens were collected to create a training set of defective and non-defective samples. Afterward, we developed several recognition models based on the ResNet architecture to find the most efficient model. In order to have a light-weight and fast inference model for real-life applicability, two structural pruning methods are applied to the classifiers. Considering the low quantity of the dataset, cross-validation and augmentation methods are used during the training. As a result, we obtained a model with more than 98% accuracy, which in comparison to the current procedure used at the company, it can recognize 20% more defective products.

关键词： computer vision Defect recognition Industrial wool X-ray

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Students' Classroom Behavior recognition Based on Behavior Pose and Attention Mechanism 6

Students' Classroom Behavior Recognition Based on Behavior P...

引用

2023 ieee 6th International conference on Information Systems and computer Aided Education, ICISCAE 2023

作者： Xia, Zhu Mingxing, Li Jiangsu University of Science and Technology School of Computer Zhenjiang China Jiangsu University Jingjiang College Zhenjiang China

ISBN: (纸本)9798350313444

Artificial intelligence technology drives the reform of traditional teaching concepts, models, content, and methods, providing assistance for the informatization and intelligence of education. Classroom teaching activities have always been a focus of research in the field of education. In complex classroom behavior recognition scenarios, it is significant to recognizing students' classroom behavior by using computer vision techniques. However, the prevailing behavior recognition methods currently demonstrate poor performance in classroom behavior recognition. Consequently, this article introduces a college student classroom behavior recognition model that integrates behavior pose and attention mechanisms to enhance the accuracy of recognizing student classroom behavior in high school classrooms. To validate the effectiveness of the proposed model, experiments were conducted using a labeled high school classroom behavior dataset. The experimental results show that the proposed model performs exceptionally well in recognizing college students' classroom behavior. © 2023 ieee.

关键词： Attention Mechanism Classroom Behavior recognition Convolutional Neural Network Deep Learning

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Text Extraction and recognition System for Myanmar Warning Signboard Images

Text Extraction and Recognition System for Myanmar Warning S...

引用

2023 ieee conference on computer Applications, ICCA 2023

作者： Zaw, Kyi Pyar War, Nu University of Computer Studies Faculty of Computer Science Mandalay Myanmar University of Computer Studies Faculty of Computer Systems and Technologies Mandalay Myanmar

ISBN: (纸本)9781665435994

Text extraction and character recognition are the computer vision tasks which became important after smart phones with good camera. Character recognition from scene text images is still challenging area, because the camera captured text images have various background noise and the text also varies in shape, font, color. In this research, a camera captured based text extraction and character recognition system is developed for Myanmar warning text images. One major challenge of this research is that Myanmar OCR system has been greatly under-researched on the camera captured images. This research therefore considers these challenges of Myanmar OCR system and proposes a new algorithm for segmentation and recognition of Myanmar script. In the character segmentation, zone-based character segmentation is performed using position and size of connected component objects. Combination of features with chain code, pixel density, a new shape-based features of boundary-centroid distance and centroid-boundary distance, are explored and exploited in recognition process. This system uses K-Nearest Neighbors (KNN) classifiers to recognize the segmented characters. From the experiment, this system achieves satisfied results 93.9% segmentation accuracy and 92.77% classification accuracy. © 2023 ieee.

关键词： Cameras

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Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings

Privacy-Preserving Image Features via Adversarial Affine Sub...

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

作者： Dusmanu, Mihai Schoenberger, Johannes L. Sinha, Sudipta N. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Menlo Pk CA USA

ISBN: (纸本)9781665445092

Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new privacy-preserving feature representation. The core idea of our work is to drop constraints from each feature descriptor by embedding it within an affine subspace containing the original feature as well as adversarial feature samples. Feature matching on the privacy-preserving representation is enabled based on the notion of subspace-to-subspace distance. We experimentally demonstrate the effectiveness of our method and its high practical relevance for the applications of visual localization and mapping as well as face authentication. Compared to the original features, our approach makes it significantly more difficult for an adversary to recover private information.

关键词： Location awareness Visualization computer vision Privacy Face recognition Authentication Rendering (computer graphics)

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Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of Torticollis 17

Automatic Assessment of Infant Face and Upper-Body Symmetry ...

引用

17th ieee International conference on Automatic Face and Gesture recognition, FG 2023

作者： Wan, Michael Huang, Xiaofei Tunik, Bethany Ostadabbas, Sarah Roux Institute Northeastern University PortlandME United States BostonMA United States Board-Certified Clinical Specialist in Pediatric Physical Therapy BostonMA United States

ISBN: (纸本)9798350345445

We apply computer vision pose estimation techniques developed expressly for the data-scarce infant domain to the study of torticollis, a common condition in infants for which early identification and treatment is critical. Specifically, we use a combination of facial landmark and body joint estimation techniques designed for infants to estimate a range of geometric measures pertaining to face and upper body symmetry, drawn from an array of sources in the physical therapy and ophthal-mology research literature in torticollis. We gauge performance with a range of metrics and show that the estimates of most these geometric measures are successful, yielding strong to very strong Spearman's p correlation with ground truth values. Furthermore, we show that these estimates, derived from pose estimation neural networks designed for the infant domain, cleanly outperform estimates derived from more widely known networks designed for the adult domain11Code and data available at https://***/ostadabbas/Infant-Upper-Body-Postural-Symmetry.. © 2023 ieee.

关键词： computer vision

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A Generalized Loss Function for Crowd Counting and Localization

A Generalized Loss Function for Crowd Counting and Localizat...

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

作者： Wan, Jia Liu, Ziquan Chan, Antoni B. City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Previous work [40] shows that a better density map representation can improve the performance of crowd counting. In this paper, we investigate learning the density map representation through an unbalanced optimal transport problem, and propose a generalized loss function to learn density maps for crowd counting and localization. We prove that pixel-wise L2 loss and Bayesian loss [29] are special cases and suboptimal solutions to our proposed loss function. A perspective-guided transport cost function is further proposed to better handle the perspective transformation in crowd images. Since the predicted density will be pushed toward annotation positions, the density map prediction will be sparse and can naturally be used for localization. Finally, the proposed loss outperforms other losses on four large-scale datasets for counting, and achieves the best localization performance on NWPU-Crowd and UCF-QNRF.

关键词： Location awareness computer vision Annotations computer architecture Cost function Bayes methods pattern recognition

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Design of Human Posture recognition System Integrating computer vision and Deep Learning Algorithm

Design of Human Posture Recognition System Integrating Compu...

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2023 International conference on Power, Electrical Engineering, Electronics and Control, PEEEC 2023

作者： Chu, Jinquan Wang, Haoyi Dong, Anxing Chen, Yuming Qufu Normal University Shandong Rizhao276800 China

ISBN: (纸本)9798350329124

In this article, a human posture recognition model combining computer vision and deep learning (DL) algorithm is proposed. Under the condition of keeping the data structure and sample labels unchanged, the model randomly cuts the video frame images by using the horizontal translation matrix, which effectively expands the data set samples, thus reducing the risk of over-fitting of the model to some extent. The experimental results show that the improved human target detection algorithm in this article can effectively improve the detection performance of the algorithm, and the computational complexity of the human posture recognition algorithm is small, which increases the correlation between adjacent video frames in the recognition process, and improves the accuracy of posture recognition while ensuring real-time. The realization of this algorithm is of great significance for building a simplified human posture recognition system with high response and accuracy. © 2023 ieee.

关键词： Object detection

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