贵州大学计算机科学与技术学院

学术报告

报告人：澳大利亚悉尼科技大学 何祥健 教授

主持人：贵州大学计算机科学与技术学院 田有亮 教授

时间：2019年11月29日 上午9:30

地点：博学楼701

Title：

      Performance-Enhancing Deep-learning Models for Crowd Counting

Abstract：

      Counting people or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem. 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), which considers the scale variation of objects in a frame adaptively, is presented. Then, a pruning strategy is presented to remove the irrelevant filters, of which feature maps contain little information, and the negative filters, which are determined by a mask learned on a training dataset. Last, a simple but efficient and effective network, named DENet, is introduced. It is composed of two components, i.e., a detection network (DNet) and an encoder-decoder estimation network (ENet). The proposed models are evaluated on the ShanghaiTech dataset, UCF dataset, WorldExpo’10 dataset etc. to show their superiority compared with the state-of-the-art methods.

何祥健 教授  澳大利亚籍

       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, together with his colleagues in a team working for a project funded by SydneyTrains and RMCRC, in 2018. He has also been awarded 'Internationally Registered Technology Specialist' by International Technology Institute (ITI). He led a UTS-PolyU joint research project team wining 1st Runner-Up prize for the 2017 VIP Cup, and the champion for the 2019 VIP Cup, awarded by IEEE Signal Processing Society.

       He has received many competitive national or regional grants including FIVE grants awarded by Australian Research Council (ARC), five grants awarded by National Natural Science Foundation of China (NSFC, and two GRF grants awarded by Hong Kong Research Grants Council (RGC). Very recently, he has received an ARC-LP grant of $540,000 for over three years from 2019-2022 and close to two-million dollars of industry grants awarded by Cisco, SAS, Sydney Trains, Data 61, RMCRC etc.

       In recent years, he has many high quality publications in prestigious journals and in premier international conferences and workshops.

       He has recently been involved in editing for various international journals. He is an Associated Editor of Springer-Nature Computer Science journal and has been an Advisor of HKIE Transactions.