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Action Status Based Novel Relative Feature Representations for Interaction Recognition

Chinese Journal of Electronics 2022年第1期31卷 168-180页

作者： LI Yanshan GUO Tianyu LIU Xing LUO Wenhan XIE Weixin ATR National Key Laboratory of Defense Technology Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Tencent

Skeleton-based action recognition has always been an important research topic in computer vision. Most of the researchers in this field currently pay more attention to actions performed by a single person while there is very little work dedicated to the identification of interactions between two people. However, the practical application of interaction recognition is actually more critical in our society considering that actions are often performed by multiple people. How to design an effective scheme to learn discriminative spatial and temporal representations for skeleton-based interaction recognition is still a challenging problem. Focusing on the characteristics of skeleton data for interactions, we first define the moving distance to distinguish the action status of the participants. Then some view-invariant relative features are proposed to fully represent the spatial and temporal relationship of the skeleton sequence. Further, a new coding method is proposed to obtain the novel relative feature representations. Finally, we design a three-stream CNN model to learn deep features for interaction recognition. We evaluate our method on SBU dataset, NTU RGB+D 60 dataset and NTU RGB+D 120 dataset. The experimental results also verify that our method is effective and exhibits great robustness compared with current state-of-the-art methods.

关键词： image representation relative feature representations skeleton-based interaction recognition skeleton sequence skeleton data image coding deep learning (artificial intelligence) image recognition temporal representations temporal relationship view-invariant relative features spatial relationship skeleton-based action recognition action status computer vision convolutional neural nets discriminative spatial representations image motion analysis

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Defendable Region for the Multiplayer Reach-Avoid Game with Free Attacker-Defender Velocity Ratio

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IFAC-PapersOnLine 2017年第1期50卷 15024-15031页

作者： Hou, Zhiwei Wang, Yongji Liu, Lei National Key Laboratory of Science and Technology on Multispectral Information Processing School of Automation Huazhong University of Science and Technology Wuhan China

The multiplayer reach-avoid game plays an important role in the applications involving cooperative agents. Analyzing the defendable region for the multiplayer reach-avoid game is the prerequisite for designing the target assignment algorithms and the control laws for both sides of the teams. Since the attacker-defender velocity ratio could be different in different application scenarios, this paper studies the defendable region for the multiplayer reach-avoid game with free attacker-defender velocity ratio. That is to say, the velocity of attacker might be less than, equal to or greater than the velocity of the defender. In order to simplify the problem, the two-dimension one defender versus one attacker issue is first dealt with. Based on the attacker-defender moving time ratio, the concept of the breakthrough point is proposed in this paper. Then, the defendable region of the one versus one reach-avoid game is analyzed based on the concept of breakthrough point. And then the defendable region of the multiplayer reach-avoid game is considered and the defendable regions are classified as strongly defendable regions and weakly defendable regions according to whether the target assignment algorithm is necessary for the defenders. Finally, we illustrate our method by some simulation results. © 2017

关键词： Velocity Application scenario Breakthrough point Cooperative agents Defendable region Multiplayers Target assignments Two dimension Velocity ratio

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Asynchronous Tag-based Physical-Layer Authentication in Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Tan, Haijun Xu, Yuntao Du, Jiewei Xie, Ning Li, Hongbin Peng Cheng Laboratory Control and Network Foundation Laboratory Frontier Research Center Shenzhen518107 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China National Innovation Center of High Speed Train Qingdao266111 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Tag-based physical-layer authentication (PLA) has gained significant research interest due to its high security and low complexity compared to traditional upper-layer authentication mechanisms. However, conventional tag-based PLA schemes often require strict synchronization between the tag and the source message before transmission, which may limit their practicality and effectiveness. To address this limitation, we propose an asynchronous tag-based PLA scheme, where the tag and the source message are transmitted asynchronously with an intentional time delay introduced at the transmitter. The proposed scheme enhances both compatibility and security without compromising robustness. We provide a theoretical analysis of the proposed scheme, and derive closed-form expressions to characterize its performance. Furthermore, theoretical comparisons with existing methods yield insightful conclusions regarding the advantages of the proposed approach. The scheme is implemented and extensively evaluated through simulations, where the theoretical results exhibit strong agreement with the simulation outcomes, thereby validating its effectiveness. © 2025 IEEE.

关键词： asynchronous closed form intentional timing delay Physical-layer authentication tag-based

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TGSIFT: Robust SIFT Descriptor Based on Tensor Gradient for Hyperspectral Images

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Chinese Journal of Electronics 2020年第5期29卷 916-925页

作者： LI Yanshan FAN Leidong XIE Weixin ATR National Key Lab.of Defense Technology Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Information Engineering Shenzhen University

This paper proposes a robust sparse descriptor based on tensor theory by using the spatial and spectral information synthetically, namely the Tensor gradient SIFT(TGSIFT), for Hyperspectral image(HSI).TGSIFT integrates both spatial and spectral information and considers the natural vector feature of HSIs. Based on the HSI Gaussian scale space, a new tensor model for HSI is proposed which takes the vectorial nature of HSI into consideration and preserves all the necessary structural information distributed over all the *** TGSIFT descriptor is formed based on the model proposed. Experimental results of HSI matching show that the TGSIFT descriptor achieves better matching performance than other SIFT descriptors under different transformations, including illumination change, sensor noise, image rotation, viewpoint change, and scale change.

关键词： tensor theory geophysical image processing tensors Tensor gradient SIFT (TGSIFT) SIFT descriptors Scale-invariant feature transform (SIFT) feature extraction HSI Gaussian scale space Sparse descriptor tensor model vectors robust SIFT descriptor spatial information image matching necessary structural information Interest points Tensor gradient SIFT transforms image rotation Gaussian processes robust sparse descriptor spectral information vectorial nature hyperspectral images Hyperspectral image (HSI) TGSIFT descriptor Hyperspectral image image representation HSI matching show

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Target Detection Based on Regional Feature Difference in Synthetic Aperture Interferometric Radiometer

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Fang, Bo Zhu, Dong Hu, Fei Xu, Yanyu Hao, Yakai Chen, Xinpeng Tao, Jingyu Min, Jiale Zheng, Bolun Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Huazhong University of Science and Technology School of Electronic Information and Communications National Key Laboratory of Science and Technology on Multispectral Information Processing Wuhan430074 China

Target detection by synthetic aperture interferometric radiometer (SAIR) has attracted attention, given its ability to provide instantaneous observation under poor visible conditions. Existing detection methods relying on a single type of feature suffer from false alarm pixels or missed targets. To address these issues, this article proposes a local region contrast-based (LRC) method using regional differences in the features of multi-order information. First, we analyze the dissimilarities in the zero-order, first-order, and second-order information using the Fisher vectors (FVs) in the differential brightness temperature (BT) images. Subsequently, we construct an FV contrast to depict target regions and avoid isolated false alarm pixels. Based on the dissimilarity characteristics of the aforementioned information features, a local region contrast feature is formulated for the target detection. Simulation and real-measured experiments validate the effectiveness of the proposed LRC method in SAIR target detection tasks. © 1980-2012 IEEE.

关键词： Synthetic apertures

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Selecting matching area based on deep learning keypoint features 12

Selecting matching area based on deep learning keypoint feat...

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SPIE 12th International Symposium on multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Ding, Tian Tian, Tian Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

Selecting the appropriate matching area is one of the crucial tasks in scene matching systems, serving as the basis for trajectory planning. Currently, there are two main approaches for adaptation zone selection: Traditional statistical feature-based adaptation zone selection methods and deep learning-based adaptation zone selection methods. The former relies on multi-Threshold screening to choose adaptation zones, resulting in a complex and less accurate process. The latter employs model training to select adaptation zones, improving accuracy, but it tends to yield less stable results. Therefore, this paper utilizes the *** network to extract keypoints and their feature descriptors within the scene region as the initial feature set. It employs support vector regression as the matching probability prediction model and uses threshold segmentation on the predicted probabilities to ultimately determine the adaptability of the region. Experimental results demonstrate that the adaptation zone selection method proposed in this paper offers higher accuracy and stability. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Navigation

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Multimodal Point Cloud Semantic Segmentation With Virtual Point Enhancement

arXiv

引用

arXiv 2025年

作者： Duan, Zaipeng Hu, Xuzhong An, Pei Ma, Jie Hubei Wuhan430074 China National Key Laboratory of Science and Technology on Multispectral Information Processing HUST Hubei Wuhan430074 China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

LiDAR-based 3D point cloud recognition has been proven beneficial in various applications. However, the sparsity and varying density pose a significant challenge in capturing intricate details of objects, particularly for medium-range and small targets. Therefore, we propose a multi-modal point cloud semantic segmentation method based on Virtual Point Enhancement (VPE), which integrates virtual points generated from images to address these issues. These virtual points are dense but noisy, and directly incorporating them can increase computational burden and degrade performance. Therefore, we introduce a spatial difference-driven adaptive filtering module that selectively extracts valuable pseudo points from these virtual points based on density and distance, enhancing the density of medium-range targets. Subsequently, we propose a noise-robust sparse feature encoder that incorporates noise-robust feature extraction and fine-grained feature enhancement. Noise-robust feature extraction exploits the 2D image space to reduce the impact of noisy points, while fine-grained feature enhancement boosts sparse geometric features through inner-voxel neighborhood point aggregation and downsampled voxel aggregation. The results on the SemanticKITTI and nuScenes, two large-scale benchmark data sets, have validated effectiveness, significantly improving 2.89% mIoU with the introduction of 7.7% virtual points on nuScenes. © 2025, CC BY.

关键词： Semantic Segmentation

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An anti-occlusion tracking algorithm based on mask strategy and adaptive response fusion 12

An anti-occlusion tracking algorithm based on mask strategy ...

引用

SPIE 12th International Symposium on multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Duan, Zhongyu Huang, Shixiang Yue, Zhongmu Tian, Tian Tian, Jinwen National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781510674943

ISBN: (纸本)9781510674936

In long-Term target tracking, target occlusion is an important factor that affects the performance of the tracker, and it is also an inevitable problem in long-Term target tracking. Therefore, it is necessary to adopt corresponding strategies for target occlusion to improve the robustness of the tracker in corresponding scenarios. In response to the problem of target loss in complex background scenarios such as target occlusion in current filtering based tracking algorithms, this paper studies the use of masking strategies and adaptive response fusion methods to improve the accuracy and robustness of tracking *** experimental results demonstrate the effectiveness of various strategies in resisting occlusion in tracking algorithms. The experimental results of the overall algorithm indicate that the algorithm can effectively solve occlusion problems while balancing accuracy, speed, and robustness. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Target tracking

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An Adaptive Parallel Gaussian Homotopy Approach for Zeroth-Order Optimization

An Adaptive Parallel Gaussian Homotopy Approach for Zeroth-O...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhou, Bingyi Wang, Guoyou Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology Wuhan China

ISBN: (纸本)9781665410205

The Gaussian homotopy method is a classical optimization approach for solving nonconvex problems. It applies Gaussian smoothing to a given problem, using varying smoothing factors to generate a series of proxy problems that are easier to solve, subsequently addressing them gradually from simpler to more complex ones. Traditional Gaussian homotopy methods typically utilize a predefined sequence or parameter to update a single smoothing factor serially. However, this sequential updating process is inefficient and unsuitable for parallel homotopy pipelines. Moreover, the utilization of predefined sequences or parameters leads to a lack of adaptability. To address the abovementioned challenges, we propose an adaptive parallel Gaussian homotopy optimization method. Initially, we introduce an adaptive model called Scaling. This model is formulated as a scaler that concurrently adjusts multiple smoothing factors, with each factor aligning to a homotopy level. It can be integrated easily into serial and parallel homotopy pipelines. Furthermore, we establish a collaborative training regimen to jointly train the Scaling model and a parallel homotopy model, named continuation path learning (CPL) [1] model. Throughout the training process, the Scaling model furnishes CPL with multiple scaled smoothing factors and updates CPL implicitly. Extensive experiments demonstrate that the proposed Gaussian homotopy approach performs competitively. © 2024 IEEE.

关键词： Gaussian distribution

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MonTransformer: Self-Supervised Phonetic to Glyph Conversion Leveraging Positional Context for Traditional Mongolian Texts

MonTransformer: Self-Supervised Phonetic to Glyph Conversion...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chenyang Zhou Monghjaya Licheng Wu School of Chinese Ethnic Minority Languages and Literatures Minzu University of China Beijing P.R.China Key Laboratory of Ethnic Language Intelligent Analysis and Security Management of MOE Minzu University of China Beijing P.R.China College of Computer Science Inner Mongolia University Hohhot P.R.China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot P.R.China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot P.R.China School of Information Engineering Minzu University of China Beijing P.R.China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The traditional Mongolian script poses significant challenges for text rendering due to its vertical orientation, complex glyph variations, and context-dependent shapes, compounded by limited linguistic resources. This paper introduces the first method specifically designed to address the out-of-vocabulary (OOV) word problem in traditional Mongolian script conversion. We present a novel dataset and propose MonTransformer, the first Transformer-based model tailored for converting Unicode phonetic sequences into glyph sequences. MonTransformer utilizes dynamic optimization and contrastive learning to effectively manage OOV cases and improve sequence generation accuracy, transforming Unicode inputs into visually accurate glyph representations. Our approach achieves state-of-the-art performance, maintaining the script’s visual fidelity and advancing efforts in digital preservation for complex scripts.

关键词： Visualization Accuracy Training data Contrastive learning Phonetics Transformers Digital preservation Speech processing Optimization Unsupervised learning

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