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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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International Conference on machine learning and Cybernetics

作者： Zhang, Weifeng Qin, Zengchang Intelligent Computing and Machine Learning Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China

ISBN: (纸本)9781424465262

The concept of label semantics[6] proposed by Lawry is a random set framework for modeling with words. It proposes two fundamental and inter-related measures of the a appropriateness of labels as descriptions of an object or value. In this paper, based on an introduction of logical expressions based on label semantics , we present a new measure to evaluate the distance between two logical expressions of fuzzy labels. Two fundamental and important properties of this distance are discussed. It is theoretically proved and tested by experiments that the new measure can explain the dissimilarity of linguistic rules well. © 2010 IEEE.

关键词： Semantics

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Wildlife Small Object Detection based on Enhanced Network in Ecological Surveillance 33

Wildlife Small Object Detection based on Enhanced Network in...

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第33届中国控制与决策会议

作者： Wan Dai Hongpeng Wang Yulin Song Yunwei Xin College of Computer Science Nankai University Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Nankai University Research Laboratory of Machine Learning and Pervasive Computing Nankai University

ISBN: (纸本)9781665431293

Visual tele-observation is an effective way for intelligent monitoring and protection of ecology and the natural *** from pedestrian or rigid body detection,wildlife detection in natural scenes face more complex problems,such as the existence of wild background clutter,local or global vegetation occlusion of animals,small object,rotation,deformation,and other interfering *** this paper,We mainly propose a novel method for the small object *** our small object wild species data set,we use an SSD detector for object ***,the K-means algorithm is used to adjust the anchor box of the SSD ***,for the situation that SSD is not good for small object detection,in conv4 layers of SSD,the feature enhancement module is ***,aiming at the size of a small object,the network level of SSD is *** have proved the feasibility of each of the three methods through experiments and combined with the three methods to verify,the recognition rate of the small target has increased by 2.67%.

关键词： anchor feature enhancement hierarchical design wildlife detection

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A robot vision system in ABU robocon contest: Visual servoing navigation and object tracking

A robot vision system in ABU robocon contest: Visual servoin...

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2nd International Conference on Materials and Products Manufacturing Technology, ICMPMT 2012

作者： Li, Xihua Zhang, Ping Lin, Sina Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China Intelligent Computing and Machine Learning Lab Beihang University Beijing 100191 China

ISBN: (纸本)9783037855447

This article took the 2011 ABU Robocon Contest as background, and introduced the design and implementation of a vision system for visual servoing navigation and object tracking in this contest. For visual servoing navigation part, it had a series of simple image processing and then added to a previous control law;tests showed only 3cm distance and 1°angle error occur when robot ran one line of 6m. For object tracking part, article introduced a combined algorithm for the detecting of a black and white semi-circle object, which moved quickly and uncertainty in a colorful background and shining environment;tests showed the proposed algorithm work better than other exist algorithms in complex environment. © (2013) Trans Tech Publications, Switzerland.

关键词： Navigation

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Clustering data and imprecise concepts

Clustering data and imprecise concepts

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Weifeng Zhang Zengchang Qin Intelligent Computing and Machine Learning Laboratory School of Automation Science and Electrical Engineering Beihang University China Robotics Institute CMU USA Intelligent Computing and Machine Learning Laboratory School of ASEE Beihang University China

Cluster analysis is the assignment of grouping a set of observations into clusters so that observations in the same cluster are similar in some sense. One of the key features for clustering is how to define a sensible similarity measure. However, classical clustering algorithms have no ability to cluster data instances and imprecise concepts using traditional distance measures. In this paper, we proposed a (dis)similarity measure based on a new knowledge representation framework called label semantics. Based on this new measure, we can automatically cluster data instance and descriptive concepts represented by logical expressions of linguistic labels. Experimental results on a toy problem in image classification demonstrate the effectiveness of the new proposed clustering algorithm. Since the new proposed measure can be extended to measuring distance between any two granularities, the new clustering algorithms can also be extended to clustering data instance and imprecise concepts represented by other granularities.

关键词： Pragmatics Clustering algorithms Semantics Image color analysis Silicon Algorithm design and analysis Humans

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Performance Optimization of Wildlife Recognition with Distributed learning in Ecological Surveillance

Performance Optimization of Wildlife Recognition with Distri...

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IEEE International Conference on Cyber Technology in Automation, Control, and intelligent Systems

作者： Wan Dai Hongpeng Wang Yulin Song Yunwei Xin Tianjin Key Laboratory of Intelligent Robotics Tianjin Research Laboratory of Machine Learning and Pervasive Computing Nankai University Tianjin

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Recently, deep learning technology has been widely used in ecological environment monitoring, but there are still huge challenges in specific tasks. For example, data sets have increased over the past few years, and in a CPU environment, training is quite time consuming. In order to solve these problems, we bring up a novel and effective method to improve training speed. In this paper, we make use of multi-GPUs to achieve distributed parallel acceleration. Small and shallow networks are not suitable for distributed training, because the computation of each parameter in this network is much higher than that of multi-layer perception or automatic encoder architecture, we use convolutional neural network with parameter sharing as the training network. In this paper, the main adopted method is to compare the performance of training wildlife recognition in single GPU and multi-GPUs environments by using distributed deep learning framework TensorFlow. The experimental results show that multi-GPUs which adopt distributed architecture can significantly accelerate training time consumption than single GPU. The results of this experiment also provides strong support for our follow-up work.

关键词： Training Parallel processing Graphics processing units Distributed databases Computational modeling Data models Neural networks

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Weifeng Zhang Zengchang Qin Intelligent Computing and Machine Learning Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China

The concept of label semantics proposed by Lawry is a random set framework for modeling with words. It proposes two fundamental and inter-related measures of the a appropriateness of labels as descriptions of an object or value. In this paper, based on an introduction of logical expressions based on label semantics, we present a new measure to evaluate the distance between two logical expressions of fuzzy labels. Two fundamental and important properties of this distance are discussed. It is theoretically proved and tested by experiments that the new measure can explain the dissimilarity of linguistic rules well.

关键词： Measurement Educational institutions

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Cross-modal topic correlations for multimedia retrieval

Cross-modal topic correlations for multimedia retrieval

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International Conference on Pattern Recognition

作者： Jing Yu Yonghui Cong Zengchang Qin Tao Wan Intelligent Computing and Machine Learning Laboratory School of ASEE Beihang University Beijing China School of Medicine Boston University USA

In this paper, we propose a novel approach for cross-modal multimedia retrieval by jointly modeling the text and image components of multimedia documents. In this model, the image component is represented by local SIFT descriptors based on the bag-of-feature model. The text component is represented by a topic distribution learned from latent topic models such as latent Dirichlet allocation (LDA). The latent semantic relations between texts and images can be reflected by correlations between the word topics and topics of image features. A statistical correlation model conditioned on category information is investigated. Experimental results on a benchmark Wikipedia dataset show that the newly proposed approach outperforms state-of-the-art cross-modal multimedia retrieval systems.

关键词： Correlation Multimedia communication Encyclopedias Electronic publishing Internet Visualization

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

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7th IFIP International Conference on intelligent Information Processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information Processing.

关键词： MapReduce

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Image scene categorization using multi-bag-of-features

Image scene categorization using multi-bag-of-features

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Weifeng Zhang Zengchang Qin Tao Wan Intelligent Computing and Machine Learning Laboratory (ICMLL) School of ASEE Beihang University China Robotics Institute Carnegie Mellon University USA

Image scene classification, the classification of images into semantic categories, e.g. city, urban, sea, etc, has recently become a vigorous research focus in computer vision for its broad application prospect. In this paper, we propose a novel approach to understand image semantic scene based on multi-bag-of-features. We aim to design an efficient but simple scene classification algorithm via fusing multiple low-level image features. Experimental results demonstrate that the proposed approach offers an effective way to classify the complex image scenes by using a multi-bag-of-features model.

关键词： Support vector machines Books Quantization Image segmentation Wide area networks

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