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Xiangjian He: A-CCNN--ADAPTIVE CCNN FOR DENSITY ESTIMATION AND CROWD COUNTING
发布日期:2019-04-10  字号:   【打印

报告时间:2019年4月19日(星期五)15:30

报告地点:翡翠湖校区科教楼B座1710室

  Xiangjian He 教授

工作单位School of Electrical and Data Engineering, University of Technology Sydney

举办单位:数学学院

报告人简介

Professor Xiangjian He is the Director of Computer Vision and Pattern Recognition Laboratory at the Global Big Data Technologies Centre(GBDTC) at the University of Technology Sydney(UTS). He is an IEEE Senior Member and has been an IEEE Signal Processing Society Student Committee member. He received a UTS Chancellor’s Award for Research Excellence in 2018. He has also been awarded ‘Internationally Registered Technology Specialist’ by International Technology Institute(ITI). He has been carrying out research mainly in the areas of image processing, network security, pattern recognition, computer vision and machine learning in the previous years. He has played various chair roles in many international conferences such as ACM MM, MMM, ICDAR, IEEE BigDataSE, IEEE TrustCom, IEEE CIT, IEEE AVSS, IEEE TrustCom, IEEE ICPR and IEEE ICARCV. He has received many competitive national or regional grants awarded by Australian Research Council(ARC), National Natural Science Foundation of China(NSFC), Hong Kong Research Grants Council(RGC). Very recently, he has received an ARC-LP grant and industry grants awarded by Cisco, SAS, Sydney Trains, Data 61, RMCRC etc. In recent years, he has many high quality publications in prestigious journals such as Journal of the Association for Information Science and Technology and ACM Computing Surveys, IEEE Transactions journals such as IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Mobile Computing, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Cloud Computing, IEEE Transactions on Reliability and IEEE Transactions on Consumer Electronics, and in Elsevier’s journals such as Pattern Recognition, Signal Processing, Automation in Construction, Information Sciences, Neurocomputing, Future Generation Computer Systems, Computer Networks, Computer and System Sciences, and Network and Computer Applications. He has also had papers published in premier international conferences and workshops such as ACL, IJCAI, CVPR, ECCV, ACM MM, TrustCom and WACV.

报告简介

Crowd counting, for estimating the number of people in a crowd using vision based computer techniques, has attracted much interest in research community. Although many attempts have been reported, real world problems, such as huge variation in subjects’ sizes in images and serious occlusion among people, make it still a challenging problem. In this talk, an Adaptive Counting Convolutional Neural Network (A-CCNN) is introduced and it takes into account the scale variation of objects in a frame adaptively so as to improve the accuracy of counting. The proposed method takes advantages of contextual information to provide more accurate and adaptive density maps and crowd counting in a scene. Extensively experimental evaluation has conducted using different benchmark datasets for object-counting, and shows that the proposed approach is effective and outperforms state-of-the-art approaches.

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