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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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Research on Improved Faster R-CNN in Stacked Artifact recognition

Research on Improved Faster R-CNN in Stacked Artifact Recogn...

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Information and Communication Technology (ICTech), international conference of

作者： Weiguang Han Xuesong Han University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

In order to improve the sorting efficiency of workpieces, improve the sorting quality, reduce the labor intensity of workers and improve the working environment, and at the same time meet the needs of small batch, multi-variety and mixed production, this paper studies the improvement of Faster R-CNN for the field of robotic sorting. Application in stacked workpiece recognition. this paper improves the Faster R-CNN and proposes the Bin-Soft-NMS algorithm based on the Soft-NMS algorithm. the dynamic threshold is used to improve the low recall rate of the model and the low score of the adjacent stacked workpiece bounding box caused by the inaccurate threshold. the problem of inhibition. A stacked workpiece dataset was made by using four kinds of workpieces, and the model was trained on the dataset to complete the experiment. the experimental results show that the average detection accuracy of the improved model in the stacked workpiece scene is 86.26%, and the improvement in mild, moderate and severe scenarios is improved respectively. increased by 1.0%, 3.2% and 4.825%, with an average increase of 3%.

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Research on Power Voice Based on WaveNet+LSTM Improvement

Research on Power Voice Based on WaveNet+LSTM Improvement

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Information and Communication Technology (ICTech), international conference of

作者： HanJun Wang MingYun Zhu Qun Wang Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang Institute of Computing Technology Shenyang China

With the rapid development of speech recognition [1] in recent years, the WaveNet model has also been discovered by more people and applied to speech recognition. In the open source LJSpeech-1.0, AISHELL and thch-30 and other general speech datasets, the model has achieved relatively good recognition results, but in the face of a large number of power voice data, because of the uniqueness and complexity of industry speech, the model is generally performed, so in this paper we have made some improvements to the model, aiming to improve the recognition of power-related speech data. First of all, in view of the shortcomings of the WaveNet network model in power speech recognition, we have improved and optimized it. the output gate structure of the LSTM network model is used to increase the nonlinearity of the model, so as to optimize the structural composition of the new model. We then introduced the core structure of the Reduced Convolution in WaveNet. According to the length of speech in the electric speech dataset, the receptive field of this structure is adjusted, and the sensory field is theoretically analyzed in order to better analyze the speech data. Finally, we experimentally verify the feasibility of applying the improved WaveNet model on the thch-30 dataset and the power-related dataset, and evaluate the trained model.

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Supervised Neighborhood Topology Learning for Human Action recognition

Supervised Neighborhood Topology Learning for Human Action R...

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international conference on Computer Vision Workshops (ICCV Workshops)

作者： Jinhua Ma Pong C Yuen Weiwen Zou Jian-Huang Lai Sun Yat-Sen University Guangzhou China Hong Kong Baptist University Hong Kong China

Supervised manifold learning has been successfully applied to human action recognition. With the class label information, the recognition performance can be improved. However, the learned manifold may not be able to well preserve the local structure which reflects temporal information of an action. To overcome this limitation, this paper proposes a new supervised manifold learning algorithm namely supervised neighborhood topology learning (SNTL) for human action recognition. SNTL is based on the framework of locality preserving projection (LPP). Different from LPP, SNTL constructs the adjacency graph with a topology defined in a supervised manner, which not only separates data points from different actions but also preserves the local structure of data points from the same action. With the advantage of locality preserving property in the framework of LPP, SNTL provides good discriminant ability and preserves temporal information of each action contained in local structure. Weizmann human action database is used for evaluation. Experimental results show that the method achieves 95.56% recognition accuracy.

关键词： Topology Humans Computer vision Data mining Optical computing Linear discriminant analysis pattern recognition conferences Sun Shape

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Distributed Edge computing System for Vehicle Communication

Distributed Edge Computing System for Vehicle Communication

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12th international conference on Data Science, Technology and Applications, DATA 2023

作者： Pakala, Rinith Kathiriya, Niket Haeri, Hossein Maddipatla, Satya Prasad Jerath, Kshitij Beal, Craig Brennan, Sean Chen, Cindy Computer Science Department University of Massachusetts Lowell 220 Pawtucket St Lowell United States Mechanical Engineering Department University of Massachusetts Lowell Lowell United States Mechanical Engineering Department The Pennsylvania State University University Park United States Mechanical Engineering Department Bucknell University Lewisburg United States

ISBN: (纸本)9789897586644

the development of communication technologies in edge computing has fostered progress across various applications, particularly those involving vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. Enhanced infrastructure has improved data transmission network availability, promoting better connectivity and data collection from IoT devices. A notable IoT application is with the Intelligent Transportation System (ITS). IoT technology integration enables ITS to access a variety of data sources, including those pertaining to weather and road conditions. Real-time data on factors like temperature, humidity, precipitation, and friction contribute to improved decision-making models. Traditionally, these models are trained at the cloud level, which can lead to communication and computational delays. However, substantial advancements in cloud-to-edge computing have decreased communication relays and increased computational distribution, resulting in faster response times. Despite these benefits, the developments still largely depend on central cloud sources for computation due to restrictions in computational and storage capacity at the edge. this reliance leads to duplicated data transfers between edge servers and cloud application servers. Additionally, edge computing is further complicated by data models predominantly based on data heuristics. In this paper, we propose a system that streamlines edge computing by allowing computation at the edge, thus reducing latency in responding to requests across distributed networks. Our system is also designed to facilitate quick updates of predictions, ensuring vehicles receive more pertinent safety-critical model predictions. We will demonstrate the construction of our system for V2V and V2I applications, incorporating cloud-ware, middleware, and vehicle-ware levels. © 2023 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Middleware

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DoppelSearch: A Novel Approach to Content-Based Video Retrieval for AI Challenge HCMC 2023 23

DoppelSearch: A Novel Approach to Content-Based Video Retrie...

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12th international Symposium on Information and Communication Technology, SOICT 2023

作者： Phan Nguyen Huu, Phong Tran Dinh, Khoa Tran Kim Ngoc, Ngan Tran Duc, Luong Le Hoang, Phuc Nguyen Huu, Quyen Phan the, Duy Pham, Van-Hau Information Security Laboratory University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh city Viet Nam Faculty of Information Science and Engineering University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Ho Chi Minh city Viet Nam

ISBN: (纸本)9798400708916

Video retrieval, which has been considered as a critical task in the field of computer vision and pattern recognition recently, finds extensive applications in several aspects such as education, entertainment, security, and healthcare. However, it faces challenges due to the complexity of video data, the instability in feature extraction methods, or the semantic disparities between videos and text. In this paper, we present a novel approach for content-based video retrieval, named DoppelSearch, leveraging the CLIP (Contrastive Language-Image Pre-training) model architecture to classify and label video segments, offering users the ability to search for videos based on specific content. Our method capitalizes on the ViT-b/32 model for feature extraction and employs feature embedding, in conjunction with the Faiss library, to enhance search efficiency. Experimental results demonstrate our model's high accuracy and swift retrieval times, promising new opportunities in content-based video retrieval for researchers, developers, and end-users. this paper not only introduces the application of the CLIP and ViT-b/32 models, but also elaborates on the specific feature extraction process and the utilization of Faiss for optimizing video retrieval. the DoppelSearch method represents a significant stride in the field of video retrieval and holds promise for diverse applications across various industries. © 2023 ACM.

关键词： Feature extraction

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A New Multimodal Video Detection Model and Dataset

A New Multimodal Video Detection Model and Dataset

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Information and Communication Technology (ICTech), international conference of

作者： Bihui Yu Jingxuan Wei Shaojie He Shuyue Tan Linzhuang Sun Chang Liu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China

In this work, we discuss the issue of multimodal detection of long and short video violations on Internet environment. We collect and annotate a dataset consisting of 6000 videos from different platforms, and offer a joint model of video frame sequences and text sequences to analyze video and text information for violation detection. We also do a comparison with the unimodal model. As far as we know, this is the first multimodal dataset proposed for the detection of violations involving “pornography and violence”. And the model adapts to the conversion of video unimodal data to multimodal data. Experiments found that although text unimodal recognition results are limited, the complementarity of the modalities in the multimodal case gives a large improvement in the results. We discuss the reasons for the results and open the codes and dataset in this domain for further study. the address is https://***/weijingxuan/multivideo.

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Efficient Regression with Feature Selection Based on L12-Norm

Efficient Regression with Feature Selection Based on L12-Nor...

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pattern recognition and Machine Learning (PRML), IEEE international conference on

作者： Yu Lei Qin Li Jane You School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China School of Telecommunications Engineering Xidian University Xi’an China Department of Computing The Hong Kong Polytechnic University Hong Kong China

Feature selection is one of the most important components of many machine learning applications. Most of the existing sparse coding-based methods only concerned with the regularization of the transformation matrix, but ignoring the sparsity of projected features in the intrinsic feature space. In this paper, we introduce a novel regularization with ℓ 12 -norm, called class specificity distribution term. It aims to ensure that samples with the same label could obey a specific distribution, while data from the different categories should have different distributions in the intrinsic feature space. then we propose a novel embedded feature selection framework based on regression and class specificity distribution term measured by ℓ 12 -norm, termed RL12. An efficient augmented Lagrange multipliers based optimization algorithm is proposed to solve the ℓ 12 -norm regularization iteratively. Experimental results demonstrate the effectiveness of our method.

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Deep Learning-Based Network Channel Quality Prediction Algorithm in High-Speed Mobile Scenarios 23

Deep Learning-Based Network Channel Quality Prediction Algor...

引用

proceedings of the 2023 12th international conference on computing and pattern recognition

作者： Heying Song Jia Guo Linying Song Luyao Wang Yexuan Zhu Shiqiang Li Wireless Computing & Systems Laboratory Tianjin Normal University China Tianjin University of technology China

ISBN: (纸本)9798400707988

As a comfortable and fast new generation popular transportation, high-speed rail(HSR) has benefited from the development of mobile Internet technology, and passengers' demand for information during HSR journeys has been increasing. At the same time, the demand for streaming media services by users is also constantly increasing. However, in high-speed mobile scenarios, streaming media transmission faces frequent network channel quality fluctuations and unpredictable issues. To address these problems, this paper proposes a channel quality prediction algorithm based on Long Short Term Memory Network (LSTM) and Convolutional Neural Network (CNN), called the CNN-LSTM model. this model can predict the available channel quality in real-time and improve the efficiency of streaming media transmission, enhancing the user's viewing experience. through extensive simulation experiments, the feasibility and effectiveness of the channel quality prediction algorithm are validated.

关键词： High-speed rail channel quality prediction deep learning network

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Semantic Filtering Global and Local Embeddings Fusion for Few-Shot Image Classification 23

Semantic Filtering Global and Local Embeddings Fusion for Fe...

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proceedings of the 2023 12th international conference on computing and pattern recognition

作者： Luyu Nong Min Wu Shanghai Key Laboratory of Trustworthy Computing East China Normal University China

ISBN: (纸本)9798400707988

Neural networks have demonstrated superior performance in image classification tasks, however, acquiring sufficient labeled data for training them in real-world scenarios remains challenging. Few-shot learning (FSL) was proposed to address this challenge in low-data scenarios and has attracted considerable attention in recent years. In few-shot image classification tasks, most of the existing FSL methods focus on designing discriminative network structures for exploring image features, either neglecting semantic information that is crucial for accurate classification, or tending to prioritize either global or local image features. To address this issue, we propose in this work a novel model Semantic Filtering Global and Local embeddings for fusion (SFGL) for few-shot image classification. SFGL effectively integrates both global and local features while leveraging semantic information. Extensive experiments conducted on benchmark datasets demonstrate that our proposed SFGL model outperforms state-of-the-art methods in FSL tasks.

关键词： few-shot learning global-local fusion image classification metric-based method semantic filter

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Parallelizing abnormal event detection in crowded scenes with GPU

Parallelizing abnormal event detection in crowded scenes wit...

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international conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Mohammadreza Yavari Maozhen Li Siguang Li Man Qi Department of Electronic and Computer Engineering Brunel University London Uxbridge UB8 3PH UK The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai China

Analyzing human activities in surveillance videos a challenging task due to the high volume of data that needs to be processed in a timely manner. this paper presents a GPU based Gaussian Mixture Model (GMM) to detect abnormal activities in crowded scenes. GMM is a fully unsupervised method that predicts abnormal crowd behaviors based on the processing of normal crowd behaviors. Specifically, we use crowd distribution and GMM to estimate the speed and to predict the behaviors of the crowd. the performance of the parallel GMM is evaluated from the aspects of computation efficiency and accuracy in terms of area under the curve.

关键词： Videos Graphics processing units Computational modeling Computer vision Surveillance Image motion analysis pattern recognition

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