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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhao, Defang Zhu, Dandan Min, Xiongkuo Yue, Jiaomin Zhang, Kaiwei Zhou, Qiangqiang Zhai, Guangtao Yang, Xiaokang CloudWalk Technology Shanghai China School of Computer Science and Technology Donghua University Shanghai China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Software Jiangxi Normal University Nanchang China Ai Institute Shanghai Jiao Tong University MOE Key Lab of Artificial Intelligence Shanghai China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The movie summarization model can automatically edit a condensed and succinct version of the movie by selecting the keyframes. Previous works mainly resort to hand-crafted heuristics and most of them are unsupervised. Supervised movie summarization is a new research field and, there is currently no publicly suitable dataset available. Moreover, existing works only focus on the movies themselves while neglecting the audiences, who have the most say in which part of the movie is more attractive. To deal with the aforementioned limitations, we establish a human attention based movie summarization dataset Movie50. Specifically, we explore the human attention variations when watching videos and have the following findings: (1) The attention of humans is concentrated when watching keyframes. (2) The attention of humans is distracted when watching non-keyframes. Inspired by these findings, we collect the eye fixations of 20 participants when watching 50 movies and propose a novel human attention based annotation pipeline. In addition, we introduce A/V-MSNet, an audiovisual neural network that takes advantage of spatio-temporal visual and auditory information to better model human attention as well as exploit more plentiful information. Extensive experiments demonstrate the superiority of the proposed method. © 2022 IEEE.

关键词： Motion pictures

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Adaptive Time-Varying Graph Learning for Traffic Flow Data Based on Anomaly Moment Detection

Adaptive Time-Varying Graph Learning for Traffic Flow Data B...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference (APSIPA)

作者： Shuhong Chen Zewei Chen Chen Li Xianwei Zheng Minfan He Xutao Li Department of Electronic Engineering Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou China School of Mathematics and Big Data Foshan University Foshan China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

This paper proposes an adaptive time-varying graph structure construction strategy for traffic flow data based on anomaly moment detection, which is capable of identifying anomaly moments within traffic flow signals and adaptively dividing the temporal dimension to construct corresponding graph representations for distinct traffic patterns. Specifically, we leverage a threshold determination method that incorporates the smoothness and energy of temporal difference traffic flow signals to identify anomaly moments in the traffic flow. Upon detecting these anomaly moments, we utilize them as segmentation boundaries along the timeline, adaptively partitioning the continuous time series into multiple dynamic and relatively stable subsequences. Within each subsequence, we apply a time-varying graph learning method to independently construct a graph structure that reflects the traffic flow characteristics of that specific period, thereby achieving an adaptive time-varying property in the traffic flow graph structure. Experimental results demonstrate that the proposed method exhibits reliable performance in detecting anomaly moments in traffic flow, and the adaptive time-varying graph structures accurately reflect the varying characteristics of traffic flow across different time periods.

关键词： Learning systems Time series analysis Asia Information processing Traffic control Data models Reliability Flow graphs Anomaly detection Periodic structures

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Incoherent receivers in alpha-stable impulsive noise

Incoherent receivers in alpha-stable impulsive noise

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MILCOM, Military communications Conference

作者： G.A. Tsihrintzis C.L. Nikias Communication Systems Lab Department of Electrical Engineering University of Virginia Charlottesville VA USA Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

We compute an incoherent receiver for demodulation of signals with random phase in additive impulsive noise modeled as a bivariate isotropic Cauchy process. Monte-Carlo simulation clearly shows that the proposed Cauchy receiver has significantly improved operating characteristic over the corresponding Gaussian receiver. Moreover, the Cauchy receiver is very robust in the entire class of bivariate isotropic symmetric alpha-stable impulsive noise. Application on real radar sea-clutter data corroborates our findings.

关键词： Interference Phase noise Demodulation Signal processing Additive noise Gaussian processes Testing Signal processing algorithms Computational modeling Noise robustness

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Video-based Human-Object Interaction Detection from Tubelet Tokens

arXiv

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arXiv 2022年

作者： Tu, Danyang Sun, Wei Min, Xiongkuo Zhai, Guangtao Shen, Wei Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

We present a novel vision Transformer, named TUTOR, which is able to learn tubelet tokens, served as highly-abstracted spatiotemporal representations, for video-based human-object interaction (V-HOI) detection. The tubelet tokens structurize videos by agglomerating and linking semantically-related patch tokens along spatial and temporal domains, which enjoy two benefits: 1) Compactness: each tubelet token is learned by a selective attention mechanism to reduce redundant spatial dependencies from others;2) Expressiveness: each tubelet token is enabled to align with a semantic instance, i.e., an object or a human, across frames, thanks to agglomeration and linking. The effectiveness and efficiency of TUTOR are verified by extensive experiments. Results shows our method outperforms existing works by large margins, with a relative mAP gain of 16.14% on VidHOI and a 2 points gain on CAD-120 as well as a 4× speedup. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Signal detection in severely heavy-tailed radar clutter

Signal detection in severely heavy-tailed radar clutter

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Asilomar Conference on Signals, Systems & Computers

作者： G.A. Tsihrintzis P. Tsakalides C.L. Nikias Communication Systems Lab Department of Electrical Engineering University of Virginia Charlottesville VA USA Signal and Image Processing Institute Department of Electrical Engineering-Systems University of Southern California Los Angeles CA USA

Alpha-stable distributions have been recognized in the signal processing community as simple, yet very accurate, two-parameter statistical models for signals and noise that contain an impulsive component of various degrees of severity. On the basis of this finding, several signal processing problems have been addressed and solved within the framework of alpha-stable distributions and with the use of fractional, lower-order statistics. We attempt to popularize these new signal processing tools within the radar community. In particular, we evaluate the goodness-of-fit of alpha-stable models in the radar environment and test the performance of new signal processing algorithms for signal detection and classification on real radar, sea-clutter data.

关键词： Signal detection Radar signal processing Interference Signal processing Object detection Radar clutter Sensor arrays Signal processing algorithms Testing Statistics

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A Research of Heart Rate Prediction Model Based on Evolutionary Neural Network

A Research of Heart Rate Prediction Model Based on Evolution...

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International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI)

作者： Feng Xiao Ming Yuchi Mingyue Ding Jun Jo Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Life College of Science and Technology Huazhong University of Science and Technology Wuhan China School of Information and Communication Technology Griffith University QLD Australia

Heart rate (HR) signal analysis is widely used in the medicine and medical research area. Physical activities (PA) are commonly recognized to greatly affect the changes of heart rate. A method of Evolutionary Neural Network - Neuro-evolution of Augmenting Topologies (NEAT) is used to build a PA-based HR predictor model. Through special coding, crossover and mutation operator, NEAT can implement network topology and connectivity weights evolution simultaneously. The common problem in evolutionary neural network, like competing conventions, how to protect the new innovation are effectively solved. The experimental results demonstrated the application potential of the approach.

关键词： Heart rate Biological neural networks Training Monitoring Neurons Encoding Biomedical monitoring

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94 GHz Three-Dimensional Imaging Radar for Environmental Detection 8

94 GHz Three-Dimensional Imaging Radar for Environmental Det...

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8th Asia-Pacific Conference on Antennas and Propagation, APCAP 2019

作者： Wu, HongMing Qi, Feng Wang, Jinkuan Northeastern University College of Information Science and Engineering Shenyang110819 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

ISBN: (纸本)9781665400541

At present, With the development of technology, the high resolution of millimeter wave (terahertz) can be applied to environmental sensing imaging. In this paper, Using the frequency-modulated continuous-wave (FMCW) radar technique with a 1GHz bandwidth, we show experimentally that a high-resolution real time imaging radar operating at 93.5-94.5GHz is capable of detecting different car models at standoff ranges of 12 m. We also achieved 3D high-resolution imaging of the building. at 140 meters. © 2019 IEEE.

关键词： Millimeter waves

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CageNeRF: cage-based neural radiance field for generalized 3D deformation and animation 22

CageNeRF: cage-based neural radiance field for generalized 3...

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Proceedings of the 36th International Conference on Neural Information processing Systems

作者： Yicong Peng Yichao Yan Shenqi Liu Yuhao Cheng Shanyan Guan Bowen Pan Guangtao Zhai Xiaokang Yang Institute of Image Communication and Network Engineering Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Alibaba Group

ISBN: (纸本)9781713871088

While implicit representations have achieved high-fidelity results in 3D rendering, it remains challenging to deforming and animating the implicit field. Existing works typically leverage data-dependent models as deformation priors, such as SMPL for human body animation. However, this dependency on category-specific priors limits them to generalize to other objects. To solve this problem, we propose a novel framework for deforming and animating the neural radiance field learned on arbitrary objects. The key insight is that we introduce a cage-based representation as deformation prior, which is category-agnostic. Specifically, the deformation is performed based on an enclosing polygon mesh with sparsely defined vertices called cage inside the rendering space, where each point is projected into a novel position based on the barycentric interpolation of the deformed cage vertices. In this way, we transform the cage into a generalized constraint, which is able to deform and animate arbitrary target objects while preserving geometry details. Based on extensive experiments, we demonstrate the effectiveness of our framework in the task of geometry editing, object animation and deformation transfer.

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Vehicle Color Recognition Using Monocular camera

Vehicle Color Recognition Using Monocular camera

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2011年无线通信与信号处理国际会议(WCSP 2011)

作者： Mengjie Yang Xiuchang Zhu Liang Li Guang Han Xiaofei Li Jiangsu Provincial Key Lab of Image Processing and ImageCommunication Nanjing Univ. of P&TNanjing Engineering Research Center of Wideband Wireless Communication and Display Technique Ministry of Ed Engineering Research Center of Wideband WirelessCommunication and Display Technique Ministry ofEduc

This paper introduces a novel moving vehicle color recognition method. By processing videos recorded by monocular camera on traffic, we get single color image of vehicles which are crossed the traffic in any direction. First, paper employed a H-S two dimension histogram method to detect the color of the vehicles, and distinguish red, yellow, green and blue four color vehicles. Due to deal with the vehicles that do not satisfy above 4 colors, Regional Color Judgment method is proposed. This method consists of the following five steps: Firstly, position the color detection region by identifying the travelling direction of the vehicle. Then pick out the candidate color extraction region used to vehicle recognition. At last extract the color of the candidate region and match the color with the templates we predefined, the color of the best fit template is the color of the vehicle.

关键词： vehicle color recognition region choice HSV space vehicle detection

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On the Training of DNN-based Average Voice Model for Speech Synthesis

On the Training of DNN-based Average Voice Model for Speech ...

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Asia-Pacific Signal and Information processing Association Annual Summit and Conference

作者： Shan Yang Zhizheng Wu Lei Xie Shaanxi Provincial Key Lab of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China The Centre for Speech Technology Research University of Edinburgh UK

ISBN: (纸本)9781509024018

Adaptability and controllability are the major advantages of statistical parametric speech synthesis (SPSS) over unit-selection synthesis. Recently, deep neural networks (DNNs) have significantly improved the performance of SPSS. However, current studies are mainly focusing on the training of speaker-dependent DNNs, which generally requires a significant amount of data from a single speaker. In this work, we perform a systematic analysis of the training of multi-speaker average voice model (AVM), which is the foundation of adaptability and controllability of a DNN-based speech synthesis system. Specifically, we employ the i-vector framework to factorise the speaker specific information, which allows a variety of speakers to share all the hidden layers. And the speaker identity vector is augmented with linguistic features in the DNN input. We systematically analyse the impact of the implementations of ivectors and speaker normalisation.

关键词： Training DNN-based Average Voice Model Speech Synthesis

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