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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Shao, Mengling Hu, Ruimin Wang, Xu Wang, Zhongyuan Xiao, Jing Gao, Ge State Key Laboratory of Software Engineering Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Computer School of Wuhan University Wuhan China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

ISBN: (纸本)9783319488950

Salient object detection in video has attracted enormous research efforts for its wide applicability. But there are still some issues in restraining the disturbance of background, which make it difficult to detect salient object in complex scenarios. Inspired by the hypothesis of center prior in image domain, we novelly introduced the concept of dynamic attention center in video. The distance between specific regions and this center is used as a weighting term to restrain the influence of background disturbance and obtain more accurate spatial and temporal saliency maps. Besides, we develop a dynamic fusion method to combine the temporal and spatial saliency map, leading to higher spatiotemporal consistency. The experiments on Freiburg-Berkeley Motion Segmentation Dataset show that our method outperforms several state-of-art methods on subjective visual perception and objective measurements. © Springer International Publishing AG 2016.

关键词： Object recognition

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Fine-grained vehicle recognition in traffic surveillance 17th

Fine-grained vehicle recognition in traffic surveillance

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Wang, Qi Wang, Zhongyuan Xiao, Jing Xiao, Jun Li, Wenbin State Key Laboratory of Software Engineering Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Computer School of Wuhan University Wuhan China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

ISBN: (纸本)9783319488899

Fine-grained vehicle recognition in traffic surveillance plays a crucial part in establishing intelligent transportation system. The major challenge lies in that differences among vehicle models are always subtle. In this paper, we propose a part-based method combining global and local feature for fine-grained vehicle recognition in traffic surveillance. We develop a novel voting mechanism to unify the preliminary recognition results, which are obtained by using Histograms of Oriented Gradients (HOG) and pre-trained convolutional neural networks (CNN), leading to fully exploiting the discriminative ability of different parts. Besides, we collect a comprehensive public database for 50 common vehicle models with manual annotation of parts, which is used to evaluate the proposed method and serves as supportive dataset for related work. The experiments show that the average recognition accuracy of our method can approach 92.3%, which is 3.4%–7.1% higher than the state-of-art approaches. © Springer International Publishing AG 2016.

关键词： Vehicles

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A stepwise frontal face synthesis approach for large pose non-frontal facial image 17th

A stepwise frontal face synthesis approach for large pose no...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Wei, Xueli Hu, Ruimin Han, Zhen Chen, Liang Ding, Xin State Key Laboratory of Software Engineering Wuhan University Wuhan China School of Computer National Engineering Research Center for Multimedia Software Wuhan University Wuhan China

ISBN: (纸本)9783319488950

Frontal face synthesis plays an important role in many fields. The existing methods mainly synthesize frontal face based on the consistency assumption that non-frontal and frontal face manifolds are locally isometric. But the assumption couldn’t be held well when non-frontal faces have large variations. To solve this problem, we propose a stepwise frontal face synthesis approach for large pose non-frontal facial image. Considering that the consistency is desirable when the angle variations of different poses are small, we divide frontal face synthesis into multiple stepwise synthesis steps. In each step, the intermediate pose training sets between non-frontal and frontal training sets are used to synthesize intermediate pose faces. Furthermore, in each step, we utilize the geometric structure of target face space with small pose as constraint to represent the input face with larger pose. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods quantitatively and qualitatively. © Springer International Publishing AG 2016.

关键词： computers

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Object tracking via online trajectory optimization with multi-feature fusion

Object tracking via online trajectory optimization with mult...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Weijian Ruan Jun Chen Chao Liang Yi Wu Ruimin Hu Wuhan University Wuhan Hubei CN National Engineering Research Center for Multimedia Software School of Computer Wuhan Univ. China Hubei Key Laboratory of Multimedia and Network Communication Engineering China and Collaborative Innovation Center of Geospatial Technology China School of Technology Nanjing Audit University China

ISBN: (纸本)9781509060689

The goal of object tracking is to estimate the state and trajectory of an interested target in a video sequence, thus both spatial and temporal information are of critical importance for tracking. However, most existing trackers usually determine targets just by the judgement like confidence from a single frame, which tend to treat tracking as a static detecting problem while neglecting the spatial-temporal relationship. In this paper, we propose a novel tracking method of online trajectory optimization with multi-feature fusion (TOFF). Considering the trajectory continuity, the accurate targets are determined by estimating the optimal short trajectories over all the video fragments, which can be obtained by the observation models that are iteratively updated based on the selected most reliable proposals. By employing the structured samples instead of binary-labeled samples, we construct a structured output model with representing descriptor of multi-feature fusion as the basic tracker. Extensive experiments on various challenging image sequences demonstrate the superiority of our method to several state-of-the-art methods.

关键词： Target tracking Proposals Trajectory optimization Reliability Feature extraction

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An analysis-oriented ROI based coding approach on surveillance video data 17th

An analysis-oriented ROI based coding approach on surveillan...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Liao, Liang Hu, Ruimin Xiao, Jing Zhan, Gen Chen, Yu Xiao, Jun State Key Laboratory of Software Engineering Wuhan University Wuhan China Computer School National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Research Institute of Wuhan University in Shenzhen Shenzhen China

ISBN: (纸本)9783319488950

Driven by the growing amount of surveillance video data, intelligent video analysis has been applied to manipulate the stored videos automatically. As the most important method of video storage, the conventional coding method with high compression ratio severely degrades the video quality, which restrict the performance of intelligent analysis. In this paper, we propose an analysis-oriented region of interest (ROI) based coding approach to relieve this problem. We qualitatively analyze the effect of video compression on the performance of video analysis, such as feature similarity and object detection. Based on the analysis, we generate the ROI by the prior knowledge of interest objects rather than considering the characteristics of Human Visual System (HVS). Then, a weight-based rate control scheme is proposed to protect the quality of ROI by assigning more bits to encode it. Experimental results show that the proposed approach can reach 5.52% and 4.39% gains average over HEVC on the performance of feature similarity and object detection respectively under the same bitrate. © Springer International Publishing AG 2016.

关键词： Object detection

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Wu, Tingzhao Hu, Ruimin Wang, Xiaochen Gao, Li Ke, Shanfa State Key Laboratory of Software Engineering Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Computer School of Wuhan University Wuhan China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Research Institute of Wuhan University in Shenzhen Shenzhen China

ISBN: (纸本)9783319488899

Head related transfer function (HRTF) is the main technique of binaural synthesis, which is used to reconstruct spatial sound image, and the HRTF data only can be obtained by measurement. A high resolution HRTF database contains too many HRTFs, the workload of measurement is too huge to be finished. As a solution, in order to calculate new HRTF by measured HRTFs, many researchers concentrate on the interpolation of HRTF. But, before interpolating, HRTFs should be aligned because there is time delay between different HRTFs. Some researchers try to implement aligning operation based on phase, but themethod is not appropriate since the periodicity of phase. Another idea to align HRTFs is by detecting method, however, the time difference is too tiny to detect exactly. None of the methods can provide a good and stable performance. In this paper, we propose a new method to align HRTFs based on correlation. And the experiments show that the proposed aligning method improves the accuracy index SDR 18.5 dB for the most, furthermore, the proposed method could improve the accuracy for all positions. © Springer International Publishing AG 2016.

关键词： Interpolation

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Criminal investigation oriented saliency detection for surveillance videos 17th

Criminal investigation oriented saliency detection for surve...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Chen, Yu Hu, Ruimin Xiao, Jing Liao, Liang Xiao, Jun Zhan, Gen State Key Laboratory of Software Engineering Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Computer School of Wuhan University Wuhan China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Research Institute of Wuhan University in Shenzhen Shenzhen China

ISBN: (纸本)9783319488899

Detecting the salient regions, namely locating the key regions that contain rich clues, is of great significance for better mining and analyzing the crucial information in surveillance videos. Yet, to date, the existed saliency detection methods are mainly designed to fit human perception. Nevertheless, what we value most during in surveillance videos, i.e. criminal investigation attentive objects (CIAOs) such as pedestrians, human faces, vehicles and license plates, is often different from those sensitive to human vision in general situations. In this paper, we proposed criminal investigation oriented saliency detection method for surveillance videos. A criminal investigation attentive model (CIAM) is constructed to score the occurrence probabilities of CIAOs in spatial domain and novelly utilize score to represent saliency, thus making CIAO regions more salient than non-CIAO regions. In addition, we refine the spatial domain saliency map with the motion information in temporal domain to obtain the spatiotemporal saliency map that has high distinctiveness for regions of moving CIAOs, static CIAOs, moving non-CIAOs and static non-CIAOs. Experimental results on surveillance video datasets demonstrate that the proposed method outperforms the state-of-art saliency detection methods. © Springer International Publishing AG 2016.

关键词： Security systems

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ULM： A user-level model for emotion prediction in social networks

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The Journal of China Universities of Posts and Telecommunications 2016年第3期23卷 63-69,88页

作者： Wang Qiyao Li Zhengmin Jin Yuehui Cheng Shiduan Yang Tan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100012 China School of Software Engineering Beijing University of Posts and Telecommunications Beijing 100876 China

Users express their emotions in online social networks （OSNs）. Studying emotions is important for understanding user behaviors. Existing methods in emotion prediction mainly use personal emotion and friend emotion to predict target emotion. In this study, public sentiment is introduced to denote the sentiment of the majority in the network. Public conformity is calculated to measure the degree of a user conforming to the public sentiment. According to the public conformity, users are categorized into three classes： Approvers, independents, and starters. A user-level model for emotion prediction is proposed to predict target emotions of different classes of users, taking into account of the public sentiment, individual sentiment, friend sentiment and pseudo-friend sentiment. Relations between the public conformity and the structure of the network are studied. Experiments conducted on Sina Weibo show that the proposed model could achieve performance improvements to some existing methods in most cases.

关键词： public sentiment conformity user-level model emotion prediction social networks

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INTELLIGENT INFORMATION network SECURITY AND MANAGEMENT

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China Communications 2016年第7期13卷 3-6页

作者： Yixian Yang Zhongshan Zhang Wenjing Li Information Security Center(ISC) Beijing University of Posts and Telecommunications(BUPT) National Engineering Laboratory for Disaster Backup and Recovery the School of Computer and Communication Engineering in the University of Science and Technology Beijing(USTB) State Key Lab of Networking and Switching Technologies Network Management Research Center at BUPT National Innovation Research Group of China

Intelligent Information network Security and Management(IINSM)is an emerging direction in future Internet architecture research,which has gained significant tractions among academia and *** the rapid development of modern society,intelligent information network with the computer as the core technology is widely used in every aspect of social ***

关键词： DCT INTELLIGENT INFORMATION network SECURITY AND MANAGEMENT Simulation DWT DCNN

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Scale-adaptive low-resolution person re-identification via learning a discriminating surface 25

Scale-adaptive low-resolution person re-identification via l...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Zheng, Wang Hu, Ruimin Yu, Yi Jiang, Junjun Chao, Liang Jinqiao, Wang State Key Laboratory of Software Engineering School of Computer Wuhan University China Digital Content and Media Sciences Research Division National Institute of Informatics Japan School of Computer Science China University of Geosciences China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China National Engineering Research Center for Multimedia Software Collaborative Innovation Center of Geospatial Technology Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

Person re-identification, as an important task in video surveillance and forensics applications, has been widely studied. But most of previous approaches are based on the key assumption that images for comparison have the same resolution and a uniform scale. Some recent works investigate how to match low resolution query images against high resolution gallery images, but still assume that the low-resolution query images have the same scale. In real scenarios, person images may not only be with low-resolution but also have different scales. Through investigating the distance variation behavior by changing image scales, we observe that scale-distance functions, generated by image pairs under different scales from the same person or different persons, are distinguishable and can be classified as feasible (for a pair of images from the same person) or infeasible (for a pair of images from different persons). The scale-distance functions are further represented by parameter vectors in the scale-distance function space. On this basis, we propose to learn a discriminating surface separating these feasible and infeasible functions in the scale-distance function space, and use it for reidentifying persons. Experimental results on two simulated datasets and one public dataset demonstrate the effectiveness of the proposed framework.

关键词： Vector spaces

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