Gait-Assisted Person Re-identification in Wide Area Surveillance

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• 作者：Apurva Bedagkar-Gala (15)
  Shishir K. Shah (15)
  
  15. Quantitative Imaging Laboratory ; Department of Computer Science ; University of Houston ; Houston ; TX ; 77204-3010 ; USA
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9010
• 期：1
• 页码：633-649
• 全文大小：5,088 KB
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• 作者单位：Computer Vision - ACCV 2014 Workshops
• 丛书名：978-3-319-16633-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Gait has been shown to be an effective feature for person recognition and could be well suited for the problem of multi-frame person re-identification (Re-ID). However, person Re-ID poses very unique set of challenges, with changes in view angles and environments across cameras. Thus, the feature needs to be highly discriminative as well as robust to drastic variations to be effective for Re-ID. In this paper, we study the applicability of gait to person Re-ID when combined with color features. The combined features based Re-ID is tested for short period Re-ID on dataset we collected using 9 cameras and 40 IDs. Additionally, we also investigate the potential of gait features alone for Re-ID under real world surveillance conditions. This allows us to understand the potential of gait for long period Re-ID as well as under scenarios where color features cannot be leveraged. Both combined and gait-only features based Re-ID is tested on the publicly available, SAIVT SoftBio dataset. We select two popular gait features, namely Gait Energy Images (GEI) and Frame Difference Energy Images (FDEI) for Re-ID and propose a sparsified representation based gait recognition method.