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Object measurement system based on Surveillance video-images 1

6th International Conference on Frontier computing, FC 2017

作者： Duan, Hui-Xian Wang, Jun Song, Lei Liu, Na Cyber Physical System R&D Center The Third Research Institute of Ministry of Public Security Shanghai201204 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Shanghai International Technology and Trade United Co. Ltd Shanghai200031 China

ISBN: (数字)9789811073984

ISBN: (纸本)9789811073977

Object measurement based on surveillance video-images is a critical task in the field of intelligent video surveillance. In this paper, we proposed an object measurement system based on surveillance video-images. Firstly, considering the scene of the urban video surveillance, a novel calibration tool is designed for such scene, which is portable, collapsible and operational. Next, based on the portable calibration tool, the intrinsic and extrinsic parameters of the surveillance camera can be estimated. Then, an object measurement system based on surveillance video-images is developed, which include the acquisition module of calibration data, the computation module of camera parameters, the selection module of measurement endpoint and the computation module of object. Finally, in the traffic checkpoint, experiments are carried out to verify the effectiveness of the object measurement system. The results show that as long as the surveillance camera is one-time calibrated based on the portable calibration tool, this system can measure the object on the surveillance video-images captured by the calibrated camera. What’s more, experimental results have shown that the object measurement system can be used to estimate the vehicle speed and measure the body height within the acceptable error range, and then have demonstrated the effectiveness of the object measurement system. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Calibration

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Artificial impostors for location privacy preservation

arXiv

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arXiv 2018年

作者： Wang, Cheng Xie, Zhiyang Department of Computer Science and Engineering Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education China

The progress of location-based services has led to severe concerns about location privacy leakage. However, existing methods are still incompetent for effective and efficient location privacy preservation (LPP). They are often vulnerable under the identification attack with side information, or hard to be implemented due to the high computational complexity. In this paper, we pursue the high protection efficacy and low computational complexity simultaneously. We propose a scalable LPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them by synthesizing artificial impostors (AIs). The so-called AIs refer to the synthesized traces that have similar semantic features to the actual traces and do not contain any target location. We devise two dedicated techniques: the sampling-based synthesis method and population-level semantic model. They respectively play the significant roles in two critical steps of synthesizing AIs. We conduct the experiments on real datasets in two cities (Shanghai of China and Asturias of Spain) to validate the high efficacy and scalability of the proposed method. In these two datasets, the experimental results show that our method achieves the preservation efficacy of 97:68% and 96:24%, and the time spent for building generators is only 230:47 and 215:92 seconds, respectively. This study would give the research community new insights into improving the practicality of the state-of-the-art LPP paradigm via counterfeiting locations. Copyright © 2018, The Authors. All rights reserved.

关键词： Location

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Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking

Vision-based Semantic Mapping and Localization for Autonomou...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Huang, Yewei Zhao, Junqiao He, Xudong Zhang, Shaoming Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

ISBN: (纸本)9781538644522

In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. High-level landmarks, the parking slots, are extracted and enriched with labels to avoid the aliasing of low-level visual features. We then proposed a robust method for detecting incorrect data associations between parking slots and further extended the optimization framework by dynamically eliminating suboptimal data associations. Visual fiducial markers are introduced to improve the overall precision. As a result, a semantic map of the parking lot can be established fully automatically and robustly. We experimented the performance of real-time localization based on the map using our autonomous driving platform TiEV, and the average accuracy of 0.3m track tracing can be achieved at a speed of 10kph. © 2018 IEEE.

关键词： Autonomous vehicles

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Greedy signal space recovery algorithm with overcomplete dictionaries in compressive sensing

arXiv

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arXiv 2019年

作者： Zhu, Jianchen Zhao, Shengjie Shi, Qingjiang Arce, Gonzalo R. College of Electronics and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Software Engineering Tongji University Shanghai201804 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. To this end, we apply a signal space greedy method, which relies on the ability to optimally project a signal onto a small number of dictionary atoms, to address signal recovery in this setting. We describe a generalized variant of the iterative recovery algorithm called Signal space Subspace Pursuit (SSSP) for this more challenging setting. Here, using the Dictionary-Restricted Isometry Property (D-RIP) rather than classical RIP, we derive a low bound on the number of measurements required and then provide the proof of convergence for the algorithm. The algorithm in noisy and noise-free measurements has low computational complexity and provides high recovery accuracy. Simulation results show that the algorithm outperforms best compared with the existing recovery algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Compressed sensing

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Observable liveness of Petri nets with controllable and observable transitions

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Science China(Information Sciences) 2017年第11期60卷 256-264页

作者： Guanjun LIU Changjun JIANG Department of Computer Science Tongji University Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University E-commerce Transactions and Information Services Collaborative Innovation Center of Shanghai University Knowledge Service Platform Tongji University

Observability and/or controllability are paid much attention to by researchers not only from the control science field[1–3]but also from the computer science field[4–9].Recently,Desel and Kilinc[4]proposed the notion of observable liveness for Petri nets with(un-)observable and(un-)controllable *** purpose of defining observable liveness is to represent that"a user can always enforce the occurrence of any observable transition only by stimulating the net by choosing appropriate enabled controllable transition"[4].An important assumption of

关键词： net cot PN Observable liveness of Petri nets with controllable and observable transitions

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A Location Privacy Preserving Scheme Based on Repartitioning Anonymous Region in Mobile Social Network

引用

Procedia Computer Science 2018年 129卷 368-371页

作者： Lina Ni Yanfeng Yuan Xiao Wang Mengmeng Zhang Jinquan Zhang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Applying the proliferated location-based services (LBS) to social networks has spawned mobile social network (MSN) services that it allows users to discover potential friends around them. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a location privacy preserving (RPAR) scheme via to repartition anonymous region where the central anonymous location minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location.

关键词： Privacy preserving mobile social network location-based service location privacy anonymous region repartition

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Hybrid Filtrations Recommendation system based on Privacy Preserving in Edge computing

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Procedia Computer Science 2018年 129卷 407-409页

作者： Lina Ni Hongdi Lin Mengmeng Zhang Jinquan Zhang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

It is challenging to design a secure recommendation system on the Internet which can help users to select their favorite products as less privacy leaked as possible. In this paper, we present a hybrid filtrations recommendation system based on privacy preserving in edge computing (HFRS-PP), which can prevent the users’ privacy information from being leaked via the merits of edge computing in the process of computing and ensure the real-time, accuracy and stability of the query results. Particularly, we propose a privacy-preserving recommendation algorithm to obtain the desired results for the end users through hybrid filtrations. The filtration-rough set theory algorithm is given to distinguish the valid reviews from spam reviews for the next filtration.

关键词： Privacy protection edge computing recommendation system rough set theory

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Forecasting of Forex Time Series Data Based on Deep Learning

引用

Procedia Computer Science 2019年 147卷 647-652页

作者： Lina Ni Yujie Li Xiao Wang Jinquan Zhang Jiguo Yu Chengming Qi College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250014 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan Shandong 250014 China Beijing Union University Beijing 100101 China

This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of deep learning algorithm for the time series data of exchange rate. We fully exploit the spatio-temporal characteristics of forex time series data based on the data-driven method. On the exchange rate data of nine major foreign exchange currencies, the experimental comparison of the forecasting method shows that the C-RNN foreign exchange time series data prediction method constructed in this paper has better applicability and higher accuracy.

关键词： Deep learning Recurrent neural network Convolutional neural network Foreign Exchange Rate Time series analysis

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Maximum delay anonymous clustering feature tree based privacy-preserving data publishing in social networks

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Procedia Computer Science 2019年 147卷 643-646页

作者： Jinquan Zhang Bowen Zhao Guochao Song Lina Ni Jiguo Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao 266590 China Shandong Province Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao 266590 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong 250014 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan Shandong 250014 China

Clustering analysis has been widely used in pattern recognition and image processing in recent years, which is an important research field of data mining. Data publishing in social networks is threatened by the leakage of private information nowadays. This paper proposes a privacy preservation scheme of sensitive data publishing in social networks based on Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to tackle this issue. The scheme is divided into an online process and an offline process. Specifically, we present the Maximum Delay Anonymous Clustering Feature (MDACF) tree data publishing algorithm.

关键词： Privacy protection Data publishing Social networks (α, L)-diversity K-anonymity Clustering feature (CF) tree

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Multi-task variational information bottleneck

arXiv

引用

arXiv 2020年

作者： Qian, Weizhu Chen, Bowei Zhang, Yichao Wen, Guanghui Gechter, Franck Le laboratoire Connaissance et Intelligence Artificielle Distribuées Université Bourgogne Franche-Comté France Adam Smith Business School University of Glasgow United Kingdom Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai200092 China Department of Systems Science School of Mathematics Southeast University Nanjing210016 China School of Engineering RMIT University MelbourneVIC3000 Australia Le laboratoire Connaissance et Intelligence Artificielle Distribuées Université Bourgogne Franche-Comté and Mosel LORIA UMR CNRS Université de Lorraine 7503 France

Multi-task learning (MTL) is an important subject in machine learning and artificial intelligence. Its applications to computer vision, signal processing, and speech recognition are ubiquitous. Although this subject has attracted considerable attention recently, the performance and robustness of the existing models to different tasks have not been well balanced. This article proposes an MTL model based on the architecture of the variational information bottleneck (VIB), which can provide a more effective latent representation of the input features for the downstream tasks. Extensive observations on three public data sets under adversarial attacks show that the proposed model is competitive to the state-of-the-art algorithms concerning the prediction accuracy. Experimental results suggest that combining the VIB and the task-dependent uncertainties is a very effective way to abstract valid information from the input features for accomplishing multiple tasks. © 2020, CC BY.

关键词： Deep learning

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