Person Re-identification by Video Ranking

  • Taiqing Wang
  • Shaogang Gong
  • Xiatian Zhu
  • Shengjin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8692)


Current person re-identification (re-id) methods typically rely on single-frame imagery features, and ignore space-time information from image sequences. Single-frame (single-shot) visual appearance matching is inherently limited for person re-id in public spaces due to visual ambiguity arising from non-overlapping camera views where viewpoint and lighting changes can cause significant appearance variation. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy image sequences of people where more reliable space-time features can be extracted, whilst simultaneously to learn a video ranking function for person re-id. Also, we introduce a new image sequence re-id dataset (iLIDS-VID) based on the i-LIDS MCT benchmark data. Using the iLIDS-VID and PRID 2011 sequence re-id datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art single-shot/multi-shot based re-id methods.


Image Sequence Action Recognition Dynamic Time Warping Gait Recognition Video Fragment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Taiqing Wang
    • 1
  • Shaogang Gong
    • 2
  • Xiatian Zhu
    • 2
  • Shengjin Wang
    • 1
  1. 1.Dept. of Electronic EngineeringTsinghua UniversityChina
  2. 2.School of EECSQueen Mary University of LondonUK

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