讲座题目：Hybrid Generative-Discriminative Hash Tracking with Spatio-Temporal Contextual Cues
主讲人：Xiangjian He 教授，悉尼科技大学
主持人：吕岳 教授
开始时间：2016-09-18 14:00
讲座地址：中北校区理科大楼B914
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) and a Leader of the Network Security research team at the Centre for Real-time Information Networks (CRIN), at the University of Technology, Sydney (UTS). He is also the Director of UTS-NPU International Joint Laboratory on Digital Media and Intelligent Networks. He is an IEEE Senior Member and has been an IEEE Signal Processing Society Student Committee member. He has 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 and computer vision in the previous years. He has played various chair roles in many international conferences such as ACM MM, MMM, IEEE TrustCom, IEEE CIT, IEEE AVSS, IEEE TrustCom, IEEE ICPR and IEEE ICARCV. In recent years, he has many high quality publications in IEEE Transactions journals such as 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 Cloud Computing, IEEE Transactions on Reliability, IEEE Transactions on Consumer Electronics, and in Elsevier’s journals such as Pattern Recognition, Signal Processing, Neurocomputing, Future Generation Computer Systems, Computer Networks, Computer and System Sciences, 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. He has recently been a guest editor for various international journals such as Journal of Computer Networks and Computer Applications (Elsevier) and Signal Processing (Elsevier). He is currently an Advisor of HKIE Transactions. Since 1985, he has been an academic, a visiting professor, an adjunct professor, a postdoctoral researcher or a senior researcher in various universities/institutions including Xiamen University, China, Shanghai Jiaotong University, China, University of New England, Australia, University of Georgia, USA, Electronic and Telecommunication Research Institute (ETRI) of Korea, University of Aizu, Japan, Hongkong Polytechnic University, and Macau University.
报告摘要：
Visual object tracking is of a great application value in video monitoring systems. Recent work on video tracking has taken into account spatial relationship between the targeted object and its background. In this paper, the spatial relationship is combined with the temporal relationship between features on different video frames so that a real-time tracker is designed based on a Hash algorithm with spatio-temporal cues. Different from most of the existing work on video tracking, which is regarded as a mechanism for image matching or image classification alone, we propose a hierarchical framework and conduct both matching and classification tasks to generate a coarse-to-fine tracking system. We develop a generative model under a modified Particle Filter with Hash fingerprints for the coarse matching by the Maximum a Posteriori (MAP) and a discriminative model for the fine classification by maximizing a confidence map based on a context model. The confidence map reveals the spatio-temporal dynamics of the target. Because Hash fingerprint is merely a binary vector and the modified Particle Filter uses only a small number of particles, our tracker has a low computation cost. By conducting experiments on 8 challenging video sequences from a public benchmark, we demonstrate that our tracker outperforms 8 state-of-the-art trackers in terms of both accuracy and speed.
