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3D POSE ESTIMATION BY GROUPED FEATURE FUSION AND MOTION AMPLITUDE ENCODING 16

3D POSE ESTIMATION BY GROUPED FEATURE FUSION AND MOTION AMPL...

引用

16th International conference on Computer Graphics, Visualization, Computer Vision and Image processing, CGVCVIP 2022, 8th International conference on Connected Smart Cities, CSC 2022, 7th International conference on Big data Analytics, data Mining and Computational Intelligence, BigDaCI 2022, and 11th International conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on Computer Science and Information Systems, MCCSIS 2022

作者： Peng, Jihua Zhou, Yanghong Mok, P.Y. Institute of Textiles and Clothing The Hong Kong Polytechnic University Hunghom Hong Kong Laboratory for Artificial Intelligence in Design Special Administrative Region Hong Kong Hong Kong

ISBN: (纸本)9789898704429

3D human pose estimation is challenging and converting it to a local pose estimation problem by dividing human body into different groups based on anatomical relationships can improve the accuracy of the resulting 3D pose estimation. Joint features of different groups are fused to predict complete pose information of entire body, in which a joint feature fusion scheme must be used for this purpose. However, the joint feature fusion adopted in existing methods has to learn a large number of parameters and is computational expensive. In this paper, we propose an optimized feature fusion (OFF) module, which requires fewer parameters and less calculations while ensures prediction accuracy. Moreover, we also propose a motion amplitude encoding (MAE) method to improve the prediction accuracy for small ranged movements. Experiments have shown that our method outperforms previous state-of-the-art results on Human3.6M dataset. © MCCSIS *** rights reserved.

关键词： signal encoding

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An ATR Method for Imbalanced data SAR Images Based on CCEGAN

An ATR Method for Imbalanced Data SAR Images Based on CCEGAN

引用

2023 International conference on Advances in Artificial Intelligence and Applications, AAIA 2023

作者： Qin, Jikai Liu, Zheng Xie, Rong Ran, Lei National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (纸本)9798400708268

Synthetic aperture radar (SAR) automatic target recognition (ATR) is an important application of SAR. In military applications, it is unrealistic to be able to acquire balanced SAR target images due to the non-cooperative nature of the targets. SAR ATR systems often face the problem of unbalanced data, which leads to the deterioration of the recognition performance of the ATR model. we propose a SAR ATR method based on a complementary cross-entropy generative adversarial network (CCEGAN), aiming to improve the robustness of the ATR model under imbalanced data. Firstly, features are extracted from the imbalanced data using a deep convolutional neural network (DCNN). Next, a classifiable GAN model is designed and then trained using features extracted from the DCNN. With the adversarial training of the GAN, the classification task is automatically accomplished while enriching the training sample set without the need for secondary screening of the generated samples. Finally, to further cope with the ATR model’s preference for majority classes, we introduce CCE as the classification loss for classifiable GAN training. This will result in a relatively balanced weighting of minority and majority classes in the loss computation, reducing the dominance of the majority class over the overall loss. The method is an end-to-end model without manually designed features. Experiments on the measured MSTAR dataset validate the robustness of the method under different imbalance rates and different minority classes. © 2023 Copyright held by the owner/author(s).

关键词： Synthetic aperture radar

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Recursive estimation techniques for detection of small objects in infrared image data

Recursive estimation techniques for detection of small objec...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： T. Soni J.R. Zeidler W.H. Ku Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA

A recursive detection scheme for point targets in infrared images is described. Estimation of the background noise is done using a weighted autocorrelation matrix update method and the detection statistic is calculated using a recursive technique. A weighting factor allows the algorithm to have finite memory and deal with nonstationary noise characteristics. The detection statistic is created by using a matched filter for colored noise, using the estimated noise autocorrelation matrix. The relationship between the weighting factor, the nonstationarity of the noise and the probability of detection is described. Some results on one- and two-dimensional infrared images are presented.< >

关键词： Recursive estimation Object detection Infrared detectors Infrared imaging Autocorrelation Statistics Colored noise Background noise Matched filters Probability

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Target Location in Distributed MIMO radar based on Coherent processing

Target Location in Distributed MIMO radar based on Coherent ...

引用

signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Fa Wei Shi Yan Shengqi Zhu Lan Lan Ximin Li Jiawei Qi Runlong Ma National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Beijing Institute of Remote Sensing Device Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In this paper, the problem of target location is investigated in the distributed multiple-input multiple-output (MIMO) radar based on coherent processing. At the modelling stage, the distributed MIMO radar is divided into three separated subarrays, where both transmit and receive functions are used in subarrays 1 and 3, while only receive function is adopted in subarray 2. Furthermore, a small frequency increment is introduced among different antenna elements in the subarray 1, in the meantime, a phase modulating operation is performed for various antenna elements in the subarray 3. In this respect, the target location performance can be improved significantly by means of the transmit modulation in subarrays 1 and 3. Under the circumstances, the model of target location is established, and a two-step estimation technology is shown, where a small location sector with respect to targets are firstly obtained, and the accurate location are further estimated. Specifically, in order to reduce the computational cost required by the multi-dimensional search procedures, an alternating procedure is developed by a sequence of 1-D searches obtained alternating between the optimization over each variable keeping the other fixed. Numerical results corroborate the effectiveness of the developed methods for the target location with distributed MIMO radar based on coherent processing.

关键词： Frequency modulation Phase modulation MIMO radar Estimation Search problems data processing Encoding Optimization Antennas MIMO communication

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Multi-resolution signal processing techniques for airborne radar

Multi-resolution signal processing techniques for airborne r...

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IEEE National conference on Radar

作者： J.S. Bergin C.M. Teixeira P.M. Techau Information Systems Laboratories Inc. Vienna VA USA

Synthetic aperture radar (SAR) exploits very high spatial resolution via temporal integration and own-ship motion to reduce the background clutter power in a given resolution cell to allow detection of non-moving targets. Ground moving target indicator (GMTI) radar, on the other hand, employs much lower resolution processing, but exploits the physical aperture and relative differences in the space-time response between moving targets and clutter for detection. Therefore, SAR and GMTl represent two different temporal processing resolution scales which have typically been optimized and demonstrated independently to work well for detecting either stationary (in the case of SAR) or exo-clutter (in the case of GMTI) targets. Based on this multi-resolution interpretation of airborne radar data processing, there appears to be an opportunity to develop detection techniques that attempt to optimize the signal processing resolution scale (e.g., length of temporal integration) to match the dynamics of a target of interest. The paper investigates signal processing techniques that exploit long CPIs (coherent processing intervals) to improve the detection performance of GMTl radar.

关键词： Radar signal processing Airborne radar Radar detection Spatial resolution signal resolution Synthetic aperture radar Motion detection Radar clutter Spaceborne radar data processing

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Optimal detection of high-velocity targets in forward scattering radar

Optimal detection of high-velocity targets in forward scatte...

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International conference on Antenna Theory and Techniques

作者： A.V. Myakinkov Information Radiosystems Department State Technical University of Nizhny Novgorod Russia

Optimal algorithm to detect very small radar cross-section high-velocity targets for bistatic forward scattering radar is considered. Structure of optimal correlation-filtering processing of forward scattering target ... 详细信息

关键词： Radar scattering Radar detection Radar cross section Doppler shift Radar tracking Chirp modulation Doppler radar signal processing Spaceborne radar Object detection

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A small Target Detection Method on Sea Surface Based on Markov Transition Field 16

A Small Target Detection Method on Sea Surface Based on Mark...

引用

16th IEEE International conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Ye, Ru Xing, Hongyan Zhou, Xing School of Electronic & Information Engineering Nanjing University of Information Science and Technology NanJing China

ISBN: (纸本)9798350327144

In response to the limitations of manually extracting features from target signals in the background of sea clutter, this paper studies Markov transition fields and convolutional neural networks, and proposes a sea small target detection method based on Markov transition fields, which transforms the detection problem into a binary classification problem. The one-dimensional observation echo is transformed into a two-dimensional image through the Markov transition field, and a sea clutter and weak signal classification model is established to complete the task of detecting weak signals in sea clutter. Taking IPIX measured radar data as the experimental object, a transfer learning model is built to autonomously learn MTF image features, improve the performance of the ResNet model, reduce training costs, compare the effects of different sampling points on detection results, and find that the effect is optimal when the sampling point is 1024. Compared with other image coding methods and classification networks, the proposed method can deeply explore the differences between targets and clutter, and has better detection and classification performance. © 2023 IEEE.

关键词： Image enhancement

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Sparse SAR Separation Imaging via Exact Observation: Initial Result

Sparse SAR Separation Imaging via Exact Observation: Initial...

引用

signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Yufan Song Hui Bi Zhefan Jin Liping Wang Zhijie Hu Jingjing Zhang College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Mathematics Nanjing University of Aeronautics and Astronautics Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In synthetic aperture radar (SAR) imaging, modulation of echo by target velocity necessitates more complex approaches for imaging moving targets. Concurrently, the scenes are generally mixed with moving and stationary targets. Achieving simultaneously separated imaging of moving and stationary targets represents a challenge in SAR imaging. In this letter, we proposed a novel separable iterative reweighted algorithm (SIRA), which can achieve separated imaging of moving and stationary targets. First, moving and stationary targets are both considered in the imaging model. Then, by separately applying sparse constraints to stationary and moving targets, the regularization problem is constructed to address the imaging of both moving and stationary targets. Finally, stationary targets are extracted by solving the Karush-Kuhn-Tucker (KKT) conditions of the problem, and moving targets are obtained through algorithm iteration. Experiments based on simulated data validate the effectiveness of the proposed method.

关键词： Sparse approximation Imaging Modulation Radar imaging data processing Radar polarimetry Iterative algorithms Sensors Synthetic aperture radar signal to noise ratio

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Hybrid Activity Recognition for Ballroom Dance Exercise using Video and Wearable Sensor 8

Hybrid Activity Recognition for Ballroom Dance Exercise usin...

引用

Joint 8th International conference on Informatics, Electronics and Vision (ICIEV) / 3rd International conference on Imaging, Vision and Pattern Recognition (icIVPR) / International conference on Activity and Behavior Computing (ABC)

作者： Matsuyama, Hitoshi Hiroi, Kei Kaji, Katsuhiko Yonezawa, Takuro Kawaguchi, Nobuo Nagoya Univ Grad Sch Engn Nagoya Aichi Japan Aichi Inst Technol Fac Informat Sci Toyota Japan

ISBN: (纸本)9781728107882

In this paper, we propose a hybrid activity recognition method for ballroom dance exercise using video and wearable sensor. The purpose of our research is to design a mechanism to support ballroom dance exercise, and this paper reports the first part to design a mechanism is to support ballroom dance exercise, and this paper reports the first outcome to achieve the purpose - recognizing ballroom dance exercise. There are two conceivable ways to recognize dance exercise: videos and wearable sensor. However, each of them has its disadvantages. Using video is a good way to recognize the movement of the body. However, it cannot provide us accurate timing or strength of foot actions because the number of their flames per seconds is too small to recognize the fast movements of dancers. On the other hand, while a wearable sensor is good at recognizing foot timing and strength, it is not good at recognizing the movement of the whole body. Therefore we propose a hybrid recognition method utilizing the merits of both video and wearable sensor. This paper focuses to recognize four different types of steps in Latin American, a kind of ballroom dance. For each step, we record wearable sensing data and videos. As a result, it is found that the accuracy of step recognition is improved by adding wearable sensing data to video data shot from two different angles.

关键词： Wearable sensors Video signal processing Machine learning Activity Recognition

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Smart Modular Few-Shot Learning Framework Changes Classification of signal Modulation

Smart Modular Few-Shot Learning Framework Changes Classifica...

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2023 International conference on data Science and Network Security, ICDSNS 2023

作者： Spoorthi, T. Tabassum, Husna Devika, S.V. Rao, K. B. V. Brahma Ramachandra, A.C. Gst Gitam University Bengaluru Campus Dept. of Cse Bengaluru India Hkbk College of Engineering Dept. of Cse Bengaluru India Hyderabad Institute of Technology and Management Department of Ece Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350301595

The purpose of this research is to categorise signal modulation using few-shot learning. The first approach is an intelligent, modular, few-shot learning framework. This system's two components are IQF and 1D-SFP, and it also has a task-driven network design. The 1D-SFP module is responsible for obtaining high-level characteristics from the raw signal, while the IQF module is in charge of retrieving low-level information. The number of 1D-SFP modules required for processing a large amount of data is calculated by studying the correlation in the hidden layer. We evaluate the suggested method using a publicly available dataset and show that it beats standard strategies in terms of accuracy. It can also learn from a small number of instances and is adaptable enough to handle innovative modulation schemes. This enables it to be used in wireless networks for data transmission and reception. The second approach uses a graph convolutional network (GCN) to calculate how many 1D-SFP modules should be integrated into a neural network architecture for a particular job. On benchmark datasets, the presented frameworks outperformed a range of state-of-the-art, conventional methodologies. Both approaches perform preprocessing, representation learning, and classification using their own distinct algorithmic frameworks. The presented strategies have the potential to increase signal modulation classification precision and efficiency in cognitive radios and other kinds of wireless communication. These gains are possible because of the potential of these techniques. © 2023 IEEE.

关键词： Learning systems

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