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A Low-Resolution Video Action Recognition Approach Based on Multi-Scale Reconstruction and Multi-Modal Fusion

IEEE Transactions on Consumer Electronics 2024年

作者： Zheng, Hui Zhao, Yesheng Zhang, Bo Shang, Guoqiang Al-Shamri, Mohammad Yahya H. Aldossary, Haya Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences Hefei Institute of Technology Innovation and Engineering Hefei230094 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Non Commissioned Officer School of Space Engineering University Beijing101416 China King Khalid University School of Computer Science Abha36311 Saudi Arabia Imam Abdulrahman Bin Faisal University Department of Computer Science Jubail31961 Saudi Arabia

The challenge of low-resolution video action recognition task lies in recovering and extracting feature representations that can effectively capture action characteristics with limited semantic information. In this paper, we propose an approach to address this challenge, which primarily comprises a multi-scale reconstruction module and a multi-modal fusion module. In multi-scale reconstruction module, we introduce a frequency-adaptive reconstruction model to reconstruct lost information from multiple scales. For crucial high-frequency sub-band images, we propose a wavelet-based super-resolution generative adversarial network to recover detailed information. In multi-modal fusion module, we propose a two-stream Transformer-based network to mine spatial-temporal joint feature representations from the reconstructed video. Additionally, we utilize another Transformer model to fuse features from different modalities, capturing both consistent and complementary representations. Finally, the fused features are fed into a classifier for recognition. Experimental results show that our proposed model outperforms other models for low-quality action recognition on HMDB51 (16×12 58.70%, 14×14 62.25%,80×60 68.94%), UCF101 (14×14 76.74%, 28×28 84.15%, 80×60 92.78%), and Tiny-VIRAT (35.63%) datasets. © 1975-2011 IEEE.

关键词： Image reconstruction

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Low-rate non-intrusive appliance load monitoring based on graph signal processing

Low-rate non-intrusive appliance load monitoring based on gr...

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2019 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2019

作者： Zhang, Bing Zhao, Shengjie Shi, Qingjiang Zhang, Rongqing Tongji University Key Laboratory of Embedded System and Service Computing of Ministry of Education Shanghai China Tongji University Sch. of Software Eng. the Key Lab. of Embedded System and Service Computing of Ministry of Education Shanghai China Tongji University School of Software Engineering Shanghai China

ISBN: (纸本)9781728159287

Thanks to the large-scale smart meters deployments around the world, non-intrusive appliance load monitoring (NILM) is receiving popularity. It aims to disaggregate the total electricity load of a home into individual appliances without resorting to any specific appliance power monitors. NILM is worthy of broad attention owing to its facilitation in energy savings. This paper regards NILM as a classification task and proposes a two-step method based on graph signal processing (GSP). In the first step, a smoothest solution is obtained by minimizing the regularization term. In the second step, gradient projection method, which uses the obtained minimizer as a start point, is adopted to optimize the while objective function, where NILM is regarded as a constrained nonlinear programming problem. The experiment results based on the open-access data set REDD clearly demonstrate that the proposed GSP-based method achieves improved performance compared with other state-of-the-art low-rate NILM approaches. © 2019 IEEE.

关键词： Signal processing

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Non-intrusive load disaggregation using semi-supervised learning method

Non-intrusive load disaggregation using semi-supervised lear...

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2019 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2019

作者： Miao, Nan Zhao, Shengjie Shi, Qingjiang Zhang, Rongqing Tongji University Key Laboratory of Embedded System and Service Computing of Ministry of Education Shanghai China Tongji University Sch. of Software Eng. the Key Lab. of Embedded System and Service Computing of Ministry of Education Shanghai China Tongji University School of Software Engineering Shanghai China

ISBN: (纸本)9781728159287

With the emerging of smart metering around the world, there is a growing demand to analyse the residential energy usage. In this paper, we propose a Deep Neural Network (DNN)-based approach for non-intrusive load monitoring (NILM), which can achieve effective and efficient estimation of individual appliance usage according to a single main meter reading in a non-intrusive manner. Considering practical situations, two training methods are provided. The first training approach is fully supervised learning, which requires a ground truth of label, indicating the state of the appliance (ON/OFF), to build a prediction model. The second training approach is semi-supervised learning, leading to better performance by F-Measure metric while only requiring some more unlabelled training data. Experimental results on the low-sample rate REDD dataset demonstrate the superior performance of our proposed DNN-based method compared with Hidden Markov Model (HMM)based and Graph Signal Processing (GSP)-based approaches. © 2019 IEEE.

关键词： Deep neural networks

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Multi-sensor Measurement Information Fusion for Maneuvering Target Recognition and Tracking

Multi-sensor Measurement Information Fusion for Maneuvering ...

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第40届中国控制会议

作者： Zheng Zhang Liang Han Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University School of Sino-French Engineer Beihang University Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education of ChinaChongqing University

This paper proposes multi-sensor information fusion algorithm for maneuvering target recognition and tracking based on mobile sensor networks（MSNs）. In order to improve target recognition accuracy, the modified evidence combination rule is designed to obtain the identity information of target based on the Dempster-Shafer（DS） algorithm. The target recognition process is designed by two stages, namely, sensing stage and fusion stage. During the first stage, each sensor node in the MSNs can obtain the feature information of the target, which makes up the measurement information. In the information fusion stage,each sensor combine the information exchanged from neighboring nodes with local measurement information to obtain the highprecise identity information of the maneuvering target based on the improved DS algorithm. Then, the identity information can be used to obtain the accurate state estimation of the maneuvering target. For the purpose of obtaining the accurate state estimation of target, the target tracking process is designed by local filtering stage and fusion stage. Finally, a numerical simulation is designed to illustrate the effectiveness of the algorithm.

关键词： Information Fusion Target Recognition and Tracking Mobile Sensor Network

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UJN-Land: A Large-Scale High-Resolution Parcel of Land of Multi-temporal Dataset with CNN Based Semantic Segmentation Analysis 7th

UJN-Land: A Large-Scale High-Resolution Parcel of Land of Mu...

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7th International Conference on Life system Modeling and Simulation, LSMS 2021, and the 7th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Shen, Yuan Wang, Yulin Yang, Shiyong Li, Yan Han, Shiyuan Liu, Zhen Xu, Tao School of Information Science and Engineering University of Jinan Jinan250022 China Highresolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Shandong Institutes of Industrial Technonlogy Jinan250102 China Land Spatial Data and Remote Sensing Technology Institute of Shandong Province Jinan250002 China

ISBN: (纸本)9789811672125

Semantic segmentation of land in remote sensing images plays an important role in urban management and rural planning, and can provide intelligent analysis for urban development. Convolutional neural network (CNN) based methods have achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, most of segmentation of land dataset were constructed based on various satellite images and samples that lack the number and diversity of existing datasets. These problems have brought challenges to the improvement and application of CNN based land parcel segmentation methods. This paper presented a large-scale high-resolution land dataset based on CNN analysis. More than 200,000 images of 256*256 pixels were obtained from Gaofen satellite images with a spatial resolution of 0.8 m, in which four category samples were manually annotated. In the experiment, three CNN based semantic segmentation methods were used as experimental baseline methods. The experimental results showed that the availability of the UJN-Land dataset is beneficial to the planning and management of cultivated land. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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Outsourced Secure Cross-Modal Retrieval based on Secret Sharing for Lightweight Clients

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IEEE Internet of Things Journal 2025年

作者： Niu, Ziyu Wang, Hao Li, Zhi Su, Ye Xu, Lijuan Zhang, Yudi Susilo, Willy Shandong Normal University School of Information Science and Engineering Jinan China University of Wollongong School of Computing and Information Technology Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education National Supercomputer Center in Jinan Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

Cross-modal retrieval is a technique that uses one modality to query another modality in multimedia data (e.g., retrieving images based on text, or retrieving text based on images). It can break down the barriers between different modalities and achieve seamless information connection. Secure cross-modal retrieval focuses on privacy issues in cross-modal retrieval, including private data of data owners and private query requests of users. Current work on secure cross-modal retrieval protects private information through homomorphic encryption, which makes the efficiency of the retrieval phase not ideal. Therefore, the conflict between retrieval efficiency and security has become an important issue that needs to be resolved in secure cross-modal retrieval. We propose a scheme to achieve secure cross-modal retrieval in the form of secret sharing in the IoT environment. In the scheme, the data owner (DO) can secretly divide all the original data into two parts and upload them to two non-collusive cloud servers respectively. The servers store the data and provide cross-modal retrieval for users. The security of the scheme is proved under semi-honest model, and the experiments show that our scheme is more efficient than previous work in the search phase. When the query dimension is 512 and the number of latent factors is 500, the search time is reduced by more than half compared with previous work. © 2014 IEEE.

关键词： Sensitive data

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Ensuring Privacy and Correlation Awareness in Multi-Dimensional service Quality Prediction and Recommendation for Iot

SSRN

引用

SSRN 2024年

作者： Zhong, Weiyi Fang, Wei Zhao, Yifan Wang, Sifeng Yan, Chao Jiang, Rong Khan, Maqbool Yang, Xuan Rafique, Wajid Qufu Normal University School of Engineering Rizhao276800 China Qufu Normal University School of Computer Science Rizhao276800 China Weifang University of Science and Technology Weifang262700 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650000 China Software Competence Center Hagenberg Upper Austria 4232 Austria Angeles University Foundation Pampanga2009 Philippines University of Calgary Department of Electrical and Software Engineering CalgaryT2N 1N4 Canada

Edge computing, with its advantages in terms of lightweight data transmission between users and cloud platforms, has become a promising solution for alleviating the heavy burden of timely data processing in many IoT scenarios, such as smart commerce and wise medicine. While several challenges are raised when we fuse the multiple-source IoT data recorded by different edge servers. First of all, data repetition within each edge server can greatly reduce the efficiency of various edge-based smart applications. Besides, IoT data fusion associated with multiple distributed edge servers can compromise user privacy. In addition, the multi-dimensional and interrelated nature of IoT data complicates precise data mining and analysis. To tackle these issues, a novel edge data fusion method (named TLTM) for cross-platform service recommendation is brought forth, which considers data dimensions, data correlation, and data privacy simultaneously. At last, to validate the effectiveness and efficiency of the TLTM method, we have designed massive experiments on the popular WS-DREAM dataset. The reported experimental results show that our TLTM method is superior to other related methods in terms of popular performance metrics including MAE, RMSE, Precision, Recall, F1-Score, and Time cost. © 2024, The Authors. All rights reserved.

关键词： Quality of service

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GRNet: Gridding Residual Network for Dense Point Cloud Completion 16th

GRNet: Gridding Residual Network for Dense Point Cloud Compl...

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16th European Conference on Computer Vision, ECCV 2020

作者： Xie, Haozhe Yao, Hongxun Zhou, Shangchen Mao, Jiageng Zhang, Shengping Sun, Wenxiu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China Faculty of Computing Harbin Institute of Technology Harbin China SenseTime Research Shenzhen China Nanyang Technological University Singapore Singapore The Chinese University of Hong Kong Hong Kong SAR China Peng Cheng Laboratory Shenzhen China SenseTime Research Hong Kong SAR China

ISBN: (纸本)9783030585440

Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. Mainstream methods (e.g., PCN and TopNet) use Multi-layer Perceptrons (MLPs) to directly process point clouds, which may cause the loss of details because the structural and context of point clouds are not fully considered. To solve this problem, we introduce 3D grids as intermediate representations to regularize unordered point clouds and propose a novel Gridding Residual Network (GRNet) for point cloud completion. In particular, we devise two novel differentiable layers, named Gridding and Gridding Reverse, to convert between point clouds and 3D grids without losing structural information. We also present the differentiable Cubic Feature Sampling layer to extract features of neighboring points, which preserves context information. In addition, we design a new loss function, namely Gridding Loss, to calculate the L1 distance between the 3D grids of the predicted and ground truth point clouds, which is helpful to recover details. Experimental results indicate that the proposed GRNet performs favorably against state-of-the-art methods on the ShapeNet, Completion3D, and KITTI benchmarks. © 2020, Springer Nature Switzerland AG.

关键词： Computer vision

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Joint resource management for MC-NOMA: A deep reinforcement learning approach

arXiv

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arXiv 2021年

作者： Wang, Shaoyang Lv, Tiejun Ni, Wei Beaulieu, Norman C. Guo, Y. Jay Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Sydney2122 Australia Beijing Key Laboratory for Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Global Big Data Technologies Centre University of Technology Sydney UltimoNSW2007 Australia

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users. © 2021, CC BY.

关键词： Resource allocation

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An iterative sinogram metal artifact reducdion based on UNet

An iterative sinogram metal artifact reducdion based on UNet

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IEEE International Conference on Big Data

作者： Zichong An Xuemei Zhu Xiangling Fu Junqi Ma Chenyi Guo Zheng Zhang School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The University Hospital of Beijing University of Posts and Telecommunications Beijing China Yofo Medical Technology Co. Ltd Beijing China Department of Electronic Enginerring Tsinghua University Beijing China School of Modern Post(School of Automation) Beijing University of Posts and Telecommunications Beijing China

In the practice of dentistry, oral dental CT images are frequently used to assist doctors in diagnosis. Filtered back projection (FBP) technique is widely employed in practice for the reconstruction of CT images obtained from X-ray calculations. However, when metal objects occur in a patient’s oral cavity, the CT images would show density discontinuities due to the metals’ “X-ray absorption coefficient is much larger than human tissues. When the FBP algorithm is applied to CT images with metals, severe metal artifacts would be obtained, which significantly reconstructed images. Therefore, metal artifact reduction (MAR) work is becoming an important problem in dentistry image processing. In this paper, we propose a novel iterative sinogram metal artifact reduction model (IS-MARM) to solve the problem. Inspired by the Diffusion model, we propose a new method to reduce metal artifacts and interpolate new data in sinogram of dentistry images iteratively. This approach reduces the difficulty of model learning and achieves good results. Secondly, we proposed a new simple method of iterative data generating to simulate real-world metals in CT sinogram images. Finally, we have demonstrated the effectiveness of our method through experiments on dental CT MAR work.

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