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

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A vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees

A Vision-based System for Traffic Anomaly Detection using De...

引用

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

作者： Aboah, Armstrong Shoman, Maged Mandal, Vishal Davami, Sayedomidreza Adu-Gyamfi, Yaw Sharma, Anuj Univ Missouri Columbia Dept Civil & Environm Engn Columbia MO 65211 USA Iowa State Univ Dept Civil & Environm Engn Ames IA USA

ISBN: (纸本)9781665448994

Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real time. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. Our approach included creating a detection model, followed by anomaly detection and analysis. YOLOv5 served as the foundation for our detection model. The anomaly detection and analysis step entail traffic scene background estimation, road mask extraction, and adaptive thresholding. Candidate anomalies were passed through a decision tree to detect and analyze final anomalies. The proposed approach yielded an F1 score of 0.8571, and an S4 score of 0.5686, per the experimental validation.

关键词： Deep learning Adaptation models Roads conferences Estimation Real-time systems pattern recognition

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Facial Emotion recognition From real-time videos using CNN Model 2

Facial Emotion Recognition From real-time videos using CNN M...

引用

2nd International conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Sharma, Swati Avasthi, Sandhya Malik, Indu Agarwal, Kadambri Department of CSE ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Facial expressions are an exciting study area within computer apparition, affecting estimating, and human-calculating interaction. Our approach offers a distinct end-to-end network for attention-based self-governing face emotion recognition. The new network design's four components are modules for feature extraction, attentiveness, and categorization and reconstruction. Through the extraction of textural information from the image and the detection of minute changes in facial expressions, Network performance can be enhanced by the properties of the Local Binary pattern. Through a mechanism for attention, the neural network may be trained to rank critical components first. The neural network may be trained to prioritize important elements using an attention mechanism. CNNs in feature extraction and pattern recognition, we aim to classify facial emotions such as joy, sorrow, rage, shock, fear, and contempt in situations that happen in real-time-expression datasets, such as FER2013 and CK+. The experimental findings confirm the feasibility and efficacy of the recommended strategy. © 2025 ieee.

关键词： Local binary pattern

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Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Image Retrieval

Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Ima...

引用

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

作者： Fuentes, Anibal Saavedra, Jose M. Impresee Inc 600 Calif St San Francisco CA 94108 USA

ISBN: (纸本)9781665448994

Architectures based on siamese networks with triplet loss have shown outstanding performance on the image-based similarity search problem. This approach attempts to discriminate between positive (relevant) and negative (irrelevant) items. However, it undergoes a critical weakness. Given a query, it cannot discriminate weakly relevant items, for instance, items of the same type but different color or texture as the given query, which could be a serious limitation for many real-world search applications. Therefore, in this work, we present a quadruplet-based architecture that overcomes the aforementioned weakness. Moreover, we present an instance of this quadruplet network, which we call Sketch-QNet, to deal with the color sketch-based image retrieval (CSBIR) problem, achieving new state-of-the-art results.

关键词： Training Visualization computer vision Image color analysis conferences Image retrieval computer architecture

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Multistage Fusion of Face Matchers

Multistage Fusion of Face Matchers

引用

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

作者： Tulyakov, Sergey Sankaran, Nishant Mohan, Deen Setlur, Srirangaraj Govindaraju, Venu Univ Buffalo Ctr Unified Biometr & Sensors Buffalo NY 14260 USA

ISBN: (纸本)9781665448994

Multistage, or serial, fusion refers to the algorithms sequentially fusing an increased number of matching results at each step and making decisions about accepting or rejecting the match hypothesis, or going to the next step. Such fusion methods are beneficial in the situations where running additional matching algorithms needed for later stages is time consuming or expensive. The construction of multistage fusion methods is challenging, since it requires both learning fusion functions and finding optimal decision thresholds for each stage. In this paper, we propose the use of single neural network for learning the multistage fusion. In addition we discuss the choices for the performance measurements of the trained algorithms and for the selection of network training optimization criteria. We perform the experiments using three face matching algorithms and IJB-A and IJB-C databases.

关键词： Training Measurement computer vision Error analysis Databases Face recognition conferences

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Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning

Continual Adaptation of Visual Representations via Domain Ra...

引用

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

作者： Volpi, Riccardo Larlus, Diane Rogez, Gregory NAVER LABS Europe Meylan France

ISBN: (纸本)9781665445092

Most standard learning approaches lead to fragile models which are prone to drift when sequentially trained on samples of a different nature-the well-known catastrophic forgetting issue. In particular, when a model consecutively learns from different visual domains, it tends to forget the past domains in favor of the most recent ones. In this context, we show that one way to learn models that are inherently more robust against forgetting is domain randomization-for vision tasks, randomizing the current domain's distribution with heavy image manipulations. Building on this result, we devise a meta-learning strategy where a regularizer explicitly penalizes any loss associated with transferring the model from the current domain to different "auxiliary" meta-domains, while also easing adaptation to them. Such meta-domains are also generated through randomized image manipulations. We empirically demonstrate in a variety of experiments-spanning from classification to semantic segmentation-that our approach results in models that are less prone to catastrophic forgetting when transferred to new domains.

关键词： Visualization Adaptation models Image segmentation computer vision Computational modeling Semantics pattern recognition

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Playing Cards Classification and Detection Using Sequential CNN Model Through Machine Learning Techniques Using Artificial Intelligence

Playing Cards Classification and Detection Using Sequential ...

引用

2024 International conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Mittal, Khushi Gill, Kanwarpartap Singh Chauhan, Rahul Sharma, Manish Sunil, G. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun248002 India School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (纸本)9798350394399

The task of identifying and classifying playing cards within images or video streams in computer vision involves employing a Sequential Convolutional Neural Network (CNN) model. This process combines image categorization and object recognition, presenting a sophisticated and engaging implementation of machine learning techniques. The significance of classifying and detecting playing cards extends to artificial intelligence and computer vision, with numerous practical applications. Systems for classifying and detecting playing cards utilizing artificial intelligence (AI) rely on computer vision methods and deep learning algorithms to achieve precise identification and categorization. These technological solutions not only enhance automation and efficiency across various tasks but also open up new possibilities for interaction and enjoyment in the domain of playing card games, making them accessible and engaging in diverse industries and user experiences. The primary objective of this research is to categorize playing cards and predict their expected accuracy, targeting a result of 75 percent. The high level of accuracy demonstrated in this study holds potential benefits for future researchers exploring this technology and subject matter. © 2024 ieee.

关键词： computer vision

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Brain-computer Interface System Based on Common Spatial patterns for Inner Speech recognition from Electroencephalography Signal by Using a Convolutional Neural Networks

Brain-Computer Interface System Based on Common Spatial Patt...

引用

2024 ieee International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Abdalla, Hussna Elnoor M. Al-Haddad, Syed Abdul Rahman Basri, Hamidon Bin Aris, Ishak Bin Yusofv, Abdul Hanif Khan Bin Neyaz, Hisham University Putra Malaysia Malaysia & Sudan International University Sudan University Putra Malaysia Department of Computer and Communication System Engineering Malaysia University Putra Malaysia Faculty of Medicine and Health Sciences Department of Medicine Malaysia University Putra Malaysia Department of Electrical and Electronic Engineering Malaysia University Putra Faculty of Medicine and Health Sciences Department of Medicine Malaysia

ISBN: (纸本)9781665410205

Inner speech recognition is a modern advancement in Brain computer interfaces (BCI) that facilitates a communication between the computer and the brain in a direct way. It is particularly beneficial for individuals who face communication issues due to speech disability. In this research, an end-to-end brain-computer interface system was developed that uses Electroencephalography (EEG) signals to recognize inner speech straight from the brain. The study utilized an open-access dataset presented on four Spanish words from ten participants. The proposed system involved preprocessing to clean and enhance the signal. Common Spatial pattern (CSP) was utilized to extract features to improve the discrimination between the selected four words. A Convolutional Neural Networks (CNNs) deep learning model was used to enhance the inner speech recognition performance from spatial features. The result shows that the model can decode the inner speech with an average accuracy of 81.40% for the un-seeing dataset and 89.80% for the entire dataset, indicating that the proposed method outperforms previous works that used the same dataset. © 2024 ieee.

关键词： Convolutional neural networks

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MASKSTR: GUIDE SCENE TEXT recognition MODELS WITH MASKING 49

MASKSTR: GUIDE SCENE TEXT RECOGNITION MODELS WITH MASKING

引用

49th ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wei, Baole He, Minghang Gao, Liangcai Zhou, Duoyou Bai, Xiang Tang, Zhi Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China ByteDance Inc Beijing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Text recognition in information loss scenarios like blurriness, occlusion, and perspective distortion is challenging in real-world applications. To enhance robustness, some studies use extra unlabeled data for encoder pretraining. Others focus on improving decoder context reasoning. However, pretraining methods require abundant unlabeled data and high computing resources, while decoder-based approaches risk over-correction. In this paper, we propose MaskSTR, a dual-branch training framework for STR models, using patch masking to simulate information loss. MaskSTR guides visual representation learning, improving robustness to information loss conditions without extra data or training stages. Furthermore, we introduce Block Masking, a novel and straightforward mask generation method, for further performance enhancement. Experiments demonstrate MaskSTR's effectiveness across CTC, attention, and Transformer decoding methods, achieving significant performance gains and setting new state-of-the-art results.

关键词： Scene text recognition OCR computer vision

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Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural Network for Sports Activity recognition with Wearable Inertial Sensors 4

Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural...

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4th International conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Imran, Hamza Ali Muhammad, Shaida Wazir, Saad Ikram, Ataul Aziz Arshad, Obaidullah National University of Sciences & Technology Department of Computing Islamabad Pakistan Korea Advanced Institute of Science & Technology School of Computing Daejeon Korea Republic of National University of Computer & Emerging Sciences School of Engineering Islamabad Pakistan

ISBN: (纸本)9798350391862

The current prevalent approach of the Internet of Health and Medical Things entails proactively preventing disease onset through routine monitoring of individuals' physical activities, making Human Activity recognition (HAR) and Behaviour Analysis an important field of study. Sports Activity recognition (SAcR) is a subset of HAR that focuses on identifying athletic movements. computer vision, Environmental Sensors, and Wearable Sensors are the three main methods for monitoring such activities. When the merits and limitations of these methods are considered, Wearable Sensors emerge as the most practical option. This study introduces the Mukhtasir-Khail-Net, a model with only 651 parameters that efficiently uses addition layers. When applied to six sports activities, the model achieves an impressive average accuracy of 98.865% using inertial sensor data generated by wearable sensors. For benchmark purposes, the presented model is also evaluated on the WISDM 11 dataset which is a Human Activity recognition dataset from Fordham University and has achieved an accuracy of 94.24%. © 2024 ieee.

关键词： Convolutional neural networks

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Learning to Detect Phone-related Pedestrian Distracted Behaviors with Synthetic Data

Learning to Detect Phone-related Pedestrian Distracted Behav...

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

作者： Hatay, Emre Ma, Jin Sun, Huiming Fang, Jianwu Gao, Zhiqiang Yu, Hongkai Cleveland State Univ Cleveland OH 44115 USA Changan Univ Xian Peoples R China

ISBN: (纸本)9781665448994

Due to the popularity and mobility of smart phones, phone-related pedestrian distracted behaviors, e.g., Texting, Game Playing, and Phone calls, have caused many traffic fatalities and accidents. As an advanced driver-assistance or autonomous-driving system, computer vision could be used to automatically detect distractions from cameras installed on the vehicle for useful safety intervention. The state-of-the-art method models this problem as a standard supervised learning method with a two-branch Convolutional Neural Network (CNN) followed by a voting on all image frames. In contrast, this paper proposes a new synthetic dataset named SYN-PPDB (448 synchronized video pairs of 53,760 computer game images) for this research problem and models it as a transfer learning problem from synthetic data to real data. A new deep learning model embedded with spatial-temporal feature learning and pose-aware transfer learning is proposed. Experimental results show that we could improve the state-of-the-art overall recognition accuracy from 84.27% to 96.67%.

关键词： computer vision Computational modeling Transfer learning Supervised learning Games pattern recognition Synchronization

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