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He has ever served as an area chair/associate editor of AVSS 2012, ICPR 2018, and BMVC 2018. He has ever joined Microsoft Research Asia Young Faculty Visiting Programme. He serves a lot for many journals and conference, and he was announced to perform outstanding review in recent top conferences (ECCV 2016 & CVPR 2017). Zheng has active research on person re-identification in the last five years. His research interests include person/object association and activity understanding in visual surveillance, and the related large-scale machine learning algorithm. He has joined the organisation of four tutorial presentations in ACCV 2012, ICPR 2012, ICCV 2013 and CVPR 2015. He has now published more than 100 papers, including more than 70 publications in major journals (TPAMI, TNN/TNNLS, TIP, TSMC-B, PR) and top conferences (ICCV, CVPR, IJCAI, AAAI).

He was a postdoctoral researcher on the EU FP7 SAMURAI Project at Queen Mary University of London and an associate professor at Sun Yat-sen University after that.

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He is now a full Professor at Sun Yat-sen University. Zheng received the PhD degree in Applied Mathematics from Sun Yat-sen University in 2008. Wei-Shi Zheng is now a Professor with Sun Yat-sen University. Extensive experimental results have shown the effectiveness of our SoDA and empirically support our theoretical analysis.

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the quotient between between-class variation and within-class variation after feature transformation) in order to investigate how the optimal feature transformation learned by SoDA sequentially approximates the offline FDA that is learned on all observed data. In particular, we derive upper and lower bounds on the Fisher discriminant score (i.e. We provide theoretical analysis on the effect of the estimated approximate within-class covariance matrix.

within-class covariance matrix) from the sketch data information. SoDA can efficiently keep the main data variations of all passed samples in a low rank matrix when processing sequential data samples, and estimate the approximate within-class variance (i.e. More specifically, we develop a Sketch online Discriminant Analysis (SoDA) by embedding sketch processing into Fisher discriminant analysis (FDA). In this work, we present an one-pass person re-id model that adapts the re-id model based on each newly observed data and no passed data are required for each update. While there is a few work on discussing online re-id, most of them require considerable storage of all passed labelled data samples that have been ever observed. However, in a real-world scenario person images detected from multi-camera system are coming sequentially, and thus these methods are not designed for processing sequential data in an online way. However, the majority of existing person re-id methods assumes all data samples are available in advance for training.

Person re-identification (re-id) is to match people across disjoint camera views in a multi-camera system, and re-id has been an important technology applied in smart city in recent years.

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