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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 Fast recognition Method for Robot Soccer Visual Images 5

Research on Fast Recognition Method for Robot Soccer Visual ...

引用

5th International conference on computers, Information Processing and Advanced Education, CIPAE 2024

作者： Long, Yaobo Pan, Leiyu Guangdong University of Science and Technology Guangdong Dongguan China Cibes Lift Jiaxing Co. Ltd Guangdong Dongguan China

ISBN: (纸本)9798331527662

Robot soccer, as an advanced field integrating artificial intelligence, image processing, and robot control, has captured the attention of numerous scholars. This study focuses on a rapid recognition method based on omnidirectional vision imagery, aiming to enhance the efficiency of soccer robots in target identification and localization. Initially, the paper explores the selection of omnidirectional vision types and their imaging influencing factors, particularly highlighting the application of catadioptric omnidirectional vision. Subsequently, it analyzes the imaging requirements for spherical targets and proposes a radial length projection model based on convex reflective surfaces. To address issues related to solving reflective surface and viewpoint change errors, this paper establishes corresponding projection functions. Furthermore, it designs a contour-based rapid region labeling algorithm and integrates convex hull restoration methods to improve the accuracy and speed of target recognition. Comparative experiments validate the effectiveness and superiority of the proposed methods. This research not only provides novel insights into visual image processing for robot soccer but also establishes a foundation for further exploration in related fields. © 2024 ieee.

关键词： Robot vision

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Micro-Expression Spotting and recognition Network based on Apex Frame 5

Micro-Expression Spotting and Recognition Network based on A...

引用

5th ieee International conference on pattern recognition and Machine Learning, PRML 2024

作者： Li, Bokai Xu, Wangwang Qin, Houying Jin, Lei Wang, Jing Zhao, Zeliang School of computer science and information engineering Hefei University of Technology Hefei China The Second Hospital of Anhui Medical University Hefei China Shanghai ninth people's hospital Shanghai Jiao Tong University School of Medicine Shanghai China

ISBN: (纸本)9798350355925

Micro-expression is an unconscious response made by people after receiving information, representing real emotions. Due to the short duration and low expression intensity, it is challenging to recognize micro-expression in the video. To this end, in this paper, we propose a novel approach based on the optical flow window to determine the position of the apex frame, which contains the most abundant emotional information. In addition, this paper proposes a Two-Stream Apex Network (TSAN), integrating complex and compelling spatiotemporal micro-expression features. The spotting network balances the micro-expression intensity by selecting the optical flow window, judges the expression states by the facial optical flow, and draws the intensity curve to obtain the apex frame. The recognition network uses the apex frame obtained by the spotting network, which is amplified and combined with the optical flow characteristics to get the micro-expression information. The spotting method can avoid the influence of frames without micro-expression in video and discard redundant features. The recognition method can better judge the category of micro-expression by combining multiple elements. This paper has extensively evaluated CASME II and CASME datasets with a comparison of state-of-the-art methods. The experimental results show that our localization method can effectively detect the position of micro-expression apex frame in the video, and the superiority of the recognition method has also been confirmed based on the apex frame. © 2024 ieee.

关键词： Complex networks

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Haris: An Advanced Autonomous Mobile Robot for Smart Parking Assistance

Haris: An Advanced Autonomous Mobile Robot for Smart Parking...

引用

2024 ieee International conference on Consumer Electronics, ICCE 2024

作者： Hamad, Layth Khan, Muhammad Asif Menouar, Hamid Filali, Fethi Mohamed, Amr Qatar Mobility Innovations Center Qatar Qatar University Department of Computer Science and Engineering Qatar

ISBN: (纸本)9798350324136

This paper presents Haris, an advanced autonomous mobile robot system for tracking the location of vehicles in crowded car parks using license plate recognition. The system employs simultaneous localization and mapping (SLAM) for autonomous navigation and precise mapping of the parking area, eliminating the need for GPS dependency. In addition, the system utilizes a sophisticated framework using computer vision techniques for object detection and automatic license plate recognition (ALPR) for reading and associating license plate numbers with location data. This information is subsequently synchronized with a back-end service and made accessible to users via a user-friendly mobile app, offering effortless vehicle location and alleviating congestion within the parking facility. The proposed system has the potential to improve the management of short-Term large outdoor parking areas in crowded places such as sports stadiums. The demo of the robot can be found on https://***/ZkTCM35fxa0?si = QjggJuN7M1o3oifx. © 2024 ieee.

关键词： Automatic License Plate recognition (ALPR) Autonomous Mobile Robot Simultaneous Localization and Mapping (SLAM) Smart Parking Assistance

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Essentials for Class Incremental Learning

Essentials for Class Incremental Learning

引用

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

作者： Mittal, Sudhanshu Galesso, Silvio Brox, Thomas Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665448994

Contemporary neural networks are limited in their ability to learn from evolving streams of training data. When trained sequentially on new or evolving tasks, their accuracy drops sharply, making them unsuitable for many real-world applications. In this work, we shed light on the causes of this well known yet unsolved phenomenon often referred to as catastrophic forgetting - in a class-incremental setup. We show that a combination of simple components and a loss that balances intra-task and inter-task learning can already resolve forgetting to the same extent as more complex measures proposed in literature. Moreover, we identify poor quality of the learned representation as another reason for catastrophic forgetting in class-IL. We show that performance is correlated with secondary class information (dark knowledge) learned by the model and it can be improved by an appropriate regularizer. With these lessons learned, class-incremental learning results on CIFAR-100 and ImageNet improve over the state-of-the-art by a large margin, while keeping the approach simple.

关键词： Learning systems computer vision Art conferences Neural networks Training data Boosting

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Facial Expression Analysis: Unveiling the Emotions Through computer vision 11

Facial Expression Analysis: Unveiling the Emotions Through C...

引用

11th International conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Uneza Gupta, Deepa Saini, Sonia Amity University India Amity University Noida India Galgotias University Greater Noida India

ISBN: (纸本)9798350350357

Facial emotion detection systems have evolved alongside advancements in computer vision and machine learning technologies, enabling the recognition and categorization of human emotions through facial expressions. These systems find applications across healthcare, marketing, content creation, and security sectors. The historical progression reflects a shift from rule-based approaches to deep learning architectures like convolutional neural networks (CNNs), improving accuracy and ***, challenges exist, including accuracy limitations, cultural diversity, biases in data and algorithms, and privacy concerns. Intrinsic and extrinsic factors such as aging, facial expressions, plastic surgery, low resolution, and noise impact system *** challenges, facial emotion detection offers advantages like refined customer insights, personalized marketing, and improved customer service. It aids psychological research and develops emotionally intelligent *** challenges requires interdisciplinary collaboration, ethical considerations, and robust methodologies. Leveraging machine learning and deep learning, along with preprocessing and optimization, facilitates the development of facial emotion detection systems to enhance user experiences responsibly and ethically. © 2024 ieee.

关键词： Emotion recognition

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Distribution Alignment: A Unified Framework for Long-tail Visual recognition

Distribution Alignment: A Unified Framework for Long-tail Vi...

引用

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

作者： Zhang, Songyang Li, Zeming Yan, Shipeng He, Xuming Sun, Jian ShanghaiTech Univ Shanghai Peoples R China Megvii Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Despite the recent success of deep neural networks, it remains challenging to effectively model the long-tail class distribution in visual recognition tasks. To address this problem, we first investigate the performance bottleneck of the two-stage learning framework via ablative study. Motivated by our discovery, we propose a unified distribution alignment strategy for long-tail visual recognition. Specifically, we develop an adaptive calibration function that enables us to adjust the classification scores for each data point. We then introduce a generalized re-weight method in the two-stage learning to balance the class prior, which provides a flexible and unified solution to diverse scenarios in visual recognition tasks. We validate our method by extensive experiments on four tasks, including image classification, semantic segmentation, object detection, and instance segmentation. Our approach achieves the state-of-the-art results across all four recognition tasks with a simple and unified framework.

关键词： Deep learning Visualization Image segmentation computer vision Semantics Object detection pattern recognition

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Evaluating the Integration of Morph Attack Detection in Automated Face recognition Systems

Evaluating the Integration of Morph Attack Detection in Auto...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Andrea Panzino Simone Maurizio La Cava Giulia Orrù Gian Luca Marcialis University of Cagliari Cagliari Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Due to the possibility of automatically verifying an individual’s identity by comparing his/her face with that present in a personal identification document, systems providing identification must be equipped with digital manipulation detectors. Morphed facial images can be considered a threat among other manipulations because they are visually indistinguishable from authentic facial photos. They can have characteristics of many possible subjects due to the nature of the attack. Thus, morphing attack detection methods (MADs) must be integrated into automated face recognition. Following the recent advances in MADs, we investigate their effectiveness by proposing an integrated system simulator of real application contexts, moving from known to never-seen-before attacks.

关键词： computer vision Face recognition conferences Employment Process control Detectors Market research

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Boosting Adversarial Robustness using Feature Level Stochastic Smoothing

Boosting Adversarial Robustness using Feature Level Stochast...

引用

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

作者： Addepalli, Sravanti Jain, Samyak Sriramanan, Gaurang Babu, R. Venkatesh Indian Inst Sci Video Analyt Lab Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (纸本)9781665448994

Advances in adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks. However, the robust accuracy of present state-of-the-art defenses is far from the requirements in critical applications such as robotics and autonomous navigation systems. Further, in practical use cases, network prediction alone might not suffice, and assignment of a confidence value for the prediction can prove crucial. In this work, we propose a generic method for introducing stochasticity in the network predictions, and utilize this for smoothing decision boundaries and rejecting low confidence predictions, thereby boosting the robustness on accepted samples. The proposed Feature Level Stochastic Smoothing based classification also results in a boost in robustness without rejection over existing adversarial training methods. Finally, we combine the proposed method with adversarial detection methods, to achieve the benefits of both approaches.

关键词： Training Deep learning computer vision Smoothing methods conferences Boosting Feature extraction

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Differentiable Rendering-based Pose-Conditioned Human Image Generation

Differentiable Rendering-based Pose-Conditioned Human Image ...

引用

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

作者： Horiuchi, Yusuke Simo-Serra, Edgar Iizuka, Satoshi Ishikawa, Hiroshi Waseda Univ Tokyo Japan Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781665448994

Conditional human image generation, or generation of human images with specified pose based on one or more reference images, is an inherently ill-defined problem, as there can be multiple plausible appearance for parts that are occluded in the reference. Using multiple images can mitigate this problem while boosting the performance. In this work, we introduce a differentiable vertex and edge renderer for incorporating the pose information to realize human image generation conditioned on multiple reference images. The differentiable renderer has parameters that can be jointly optimized with other parts of the system to obtain better results by learning more meaningful shape representation of human pose. We evaluate our method on the Market-1501 and DeepFashion datasets and comparison with existing approaches validates the effectiveness of our approach.

关键词： Heating systems computer vision Image synthesis Shape Image edge detection conferences Training data

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Multisport Dynamics Explored Through Advanced Activity recognition Techniques in Athletic Performance 8

Multisport Dynamics Explored Through Advanced Activity Recog...

引用

8th ieee International conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Salian, Supriya Preethi Salian, K. D'Souza, Ria Monterio, Rencita Fernandes, Rinette Veronica Almeida, Riya D. St. Joseph Engineering College Vamanjoor Dept. of Computer Science and Engineering Mangaluru India Dept. of Information Science and Engineering Udupi India

ISBN: (纸本)9798350350593

recognition of human activities has drawn a lot of interest lately in the field of computer vision and machine learning. Group activity recognition is a significant subcategory in which several people participate in a common activity. The primary obstacle in these tasks is learning the relationships between individuals in a scene and understanding their evolution over time. The suggested study offers a new taxonomy to classify state-of-the-art (SOTA) group activity recognition approaches and subcategorizes the current literature. It also critically analyzes these techniques. To understand scenes involving multiple people, models must describe individual actions in context and infer collective activities. Accurately capturing relationships between actors and performing relational reasoning is crucial for comprehending group activities. However, modelling these relationships is challenging due to the limited availability of interaction information, relying only on individual action labels and collective activity labels. Inferring relationships from other aspects is thus essential. Group activity recognition has garnered increased research attention recently due to its importance in video understanding. The difficulties lie not only in recognizing individual actions but also in exploring scene information and collaborative relations among people. This research addresses these challenges by providing a comprehensive review of existing techniques, offering a structured approach to categorize them, and highlighting the importance of relational reasoning in understanding group activities. © 2024 ieee.

关键词： Active learning

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